load_rollout_vs_truth was including rollout.py's synthetic termination-
bookkeeping rows (escaped/unknown_pdg/energy_cutoff/max_steps) unfiltered:
these carry step_length=0 and edep=pre_E dumped in one row for shower-level
energy conservation, not a real per-step value, and nearly doubled the
apparent mean edep in a repro. _load_world_frame_side now drops them, keeping
only real generated steps (continuing or natural_end). Also documents the
remaining, unfixable difference: rollout's edep on real steps absorbs any
secondary-energy budget Stage 2 didn't allocate, which truth's edep never does.
Adds compute_truth_observables, the truth-schema counterpart to
compute_rollout_observables, so the Tier 4 event-level plots
(plot_rollout_longitudinal/transverse/total_energy) can overlay a real
reference computed directly from load_rollout_vs_truth's own truth file,
without needing a separate paired giant predict --coord local file. Shares
the depth/transverse binning core with compute_rollout_observables via a new
_event_axis_depth_transverse helper.
Updates rollout_validation.ipynb's Tier 4 section to use this reference and
points ROLLOUT_FILE/TRUTH_FILE at a real prediction/shard pair.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Extends the Tier 1-3 SampleCollection diagnostics (marginals, correlations,
pairwise, direction alignment, constraints) to work on a full autoregressive
giant rollout shower checked against an independent ground-truth steps file,
rather than only paired giant predict --coord local output. The two files
are unpaired (different lengths, own conditioning), so SampleCollection
gains optional *_gen fields and _group_labels/marginal_table/plot_marginals/
plot_pairwise build independent real/gen masks instead of assuming one.
Adds analysis/rollout_validation.ipynb, a sibling of validation.ipynb built
around this workflow.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Closes the loop from single-step prediction into full showers:
- giant/geometry.py + `dwarf build-geometry-oracle`: learn position ->
(material, layer_id) from data (KNN/SVM) to supply the conditioning the
surrogate does not predict; flag detector escape by NN distance.
- giant/rollout.py: breadth-first batched frontier that steps all active
tracks, spawns secondaries as new tracks, and terminates on energy cutoff,
per-track max steps, escape, or natural end. Energy is deposited locally on
every stop except escape (leakage), so showers conserve energy exactly.
- `giant rollout` CLI: seed from real events (argmax pre_E), load checkpoint,
write a world-frame steps parquet + YAML sidecar.
- giant/analysis.py: compute_rollout_observables + plot_rollout_* for
single-sided longitudinal/transverse/total-energy shower profiles;
analysis/export_rollout_observables.py driver.
- scikit-learn added as an optional `geometry` extra (lazy-imported).
- Tests: tests/test_geometry.py, tests/test_rollout.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Brings the energy-conservation PoC work (dwarf CLI unification, dwarf
status improvements, predict --comment, ODE-step comparison scripts,
predict-parquet-only analysis refactor) onto the Phase 2 branch.
Conflict resolution:
- giant/analysis.py: took the energy-conservation-poc version wholesale.
That branch deliberately removed the live checkpoint+sampler diagnostics
path (ModelBundle/load_model_bundle/make_val_loader/collect_samples) in
favor of reading `giant predict --coord local` parquet output. Phase 2's
only edits to this file adapted the removed path to the new dataset API,
so nothing Phase-2-specific is lost; no external code called those funcs.
Fixes for pre-existing breakage surfaced by the merge (both predate it):
- giant/cli.py: predict's `_process` unpacked build_features into 5 values,
but Phase 2 made it return 8 (added n_sec/sec_cont/sec_pdg_idx). Expanded
the unpack; `giant predict --coord local` would have crashed otherwise.
- tests/test_steps_to_parquet.py: Phase 2 renamed _add_secondary_energy ->
_add_secondary_attributes without updating this test. Renamed the calls
and extended the fixture with the pdg/pre_d{x,y,z} columns the expanded
function reads; e_sec assertions unchanged.
- analysis/compare_ode_steps_energy_conservation.py: E731 lambda assignment
(added in the un-linted final PoC commit) rewritten as a def.
ruff, ty, and pytest (179 passed) all green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add analysis scripts for the 10-vs-20 flow-matching ODE-step ablation on the
energy-conservation PoC predict outputs:
- compare_ode_steps_energy_conservation.py: per-event energy-budget table +
20-step plots and the 10-vs-20 overlay.
- compare_ode_steps_kl.py: per-step marginal KL(real||gen) per target dim over
fixed shared bins, so the two runs are directly comparable dim-by-dim.
Also commit export_energy_conservation_poc.py (the baseline event-level budget
export) and repoint validation.ipynb at the PoC predict file at sample_frac=1.0.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Removes unused imports and an ambiguous variable name, narrows
Optional types before use so ty's flow analysis is satisfied, swaps
sum() over polars expressions for pl.sum_horizontal to avoid the
Literal[0] fallback type, and converts numpy bin edges to plain lists
before passing to matplotlib's hist (whose stub only accepts
Sequence[float]). Also applies ruff format across the repo, which had
drifted out of sync with the formatter.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
One-off script (like export_validation_plots.py / export_event_observables.py)
that writes a 3x3 marginals grid, post_dir/travel_dir norm histograms, and
vector-PDF copies of the KL-bars/photon-edep/event-level plots directly into
the thesis-presentations repo's images/ folder.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Move ipykernel into the analysis extra instead of a separate
dependency group, since it's needed wherever analysis plotting runs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
One-off script mirroring export_validation_plots.py, used to export the
new event-level and pdg-contribution-share plots into the knowledge-base
attachments folder.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
pdg_contribution_table_pl sums real/generated total deposited energy and
total step_length per pdg species over the whole file (pure lazy polars
group_by, no post_pos reconstruction needed for these scalars). Adds
plot_pdg_energy_share/plot_pdg_length_share, each rendering two pies
(real vs generated) so the per-species breakdown can be compared directly,
plus a matching notebook section.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
sum(step_length) per event_id, alongside the existing total deposited
energy, since path length and energy deposit aren't interchangeable once
tracks scatter. Adds plot_total_length and a matching notebook cell.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds section headers and explanatory comments throughout the existing
tiers, plus new cells running compute_event_observables_pl and the four
event-level shower plots. Notebook outputs reflect the user's own re-run.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds plot_kl_bars/plot_kl_bars_pl (numpy/polars variants) for ranking which
target dimension or pdg/material stratum drives KL regressions, with the
same kl*n group capping as plot_marginals. Switches existing histogram
plots to step-type/log-scale. Adds sample_frac to load_predicted_local for
subsampling large predict parquets.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
lars