_bin_expr clipped the bin index to [0, nbins-1] only after casting it to
Int32, so the clip never got the chance to do its job: a rollout
step_length of 1.0725e10 mm against fixed edges [2.9e-5, 94.04] with 50
bins gives a raw index of ~5.7e9, which overflows i32 and fails the
strict cast, killing the whole compute-one job. Same for +/-inf.
Clamp in f64 first and cast after. NaN has no edge to clamp to, so map
it to null and drop it in the two callers (hist1d, profile_partial) —
what np.histogram does with it, and what profile_partial needs anyway
since a null bin index would break its np.add.at.
Partials computed before this change stay valid: the old code crashed on
these values rather than binning them wrong, so any chunk that produced
a partial contained none of them and its counts are unchanged here.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Replace the monolithic giant/analysis.py (predict-local + RolloutVsTruth
diagnostics) with a lean giant/analysis/ package that compares one
autoregressive `giant rollout` for a checkpoint against a held-out
miniCaloSim reference file, and generates publication-styled plots in
parallel on HTCondor.
Rollout output and a raw reference file share a world-frame physical
column subset under identical names, so the old ALR/local-frame decode
machinery is gone — everything is world-frame mm/MeV.
- sources.py: canonical LazyFrames, synthetic-termination-row filtering,
the secondary view (rollout generation>0 tracks vs reference sec_*_list).
- reduce.py: streaming primitives — a single hist1d group_by pass, per-event
scalars, edep-weighted depth/transverse profiles, species share, leakage.
- context.py/grouping.py: prep resolves fixed bin edges + energy/pdg/material
group sets once into shared.json, so each compute job is one pass, no range
scan (histogram efficiency).
- catalog.py: declarative PlotSpec registry — marginals x {overall,energy,pdg,
material}, per-event totals, shower profiles, species/leakage, secondaries.
- render.py: the only plotstyle/LaTeX importer; PDFs + gallery metadata.
- condor.py + `giant analyze` CLI (prep/compute-one/list/render/submit):
one job per plot, compute/render split (workers polars-only, no LaTeX).
Styling via ETPlot's plotstyle (added to the analysis extra). New tests cover
the reduce primitives, catalog id uniqueness + compute, condor submit, and a
guarded render smoke test. Delete the two predict-diagnostics notebooks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
lars