Add generic auxiliary array bundles to the fit input HDF5#145
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Mirror the external_terms mechanism so ParamModels can carry pre-computed
arrays (e.g. a reco x gen response matrix) inside the datacard rather than
reading them from a separately-versioned file at fit time.
- rabbit/auxiliary.py: write_auxiliary_group / read_auxiliary_from_h5.
Numeric arrays round-trip bit-for-bit (dtype + shape, incl. >2D) via
writeFlatInChunks/maketensor; 1-D string lists round-trip as list[str]
via a vlen-str dataset.
- TensorWriter.add_auxiliary(name, datasets) + write() serialization under
a top-level "auxiliary" group (duplicate-name guarded).
- FitInputData.auxiliary: {name: {key: ndarray|list[str]}}; {} when the
group is absent (back-compatible read).
- tests/test_auxiliary.py: round-trip through write()+FitInputData, the
no-auxiliary and duplicate-name cases, and a direct mixed-dtype round-trip.
Not used by the fit itself; a side channel for ParamModels.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Carry the reco x gen response matrix R (and the gen-total N_gen) through the setupRabbit datacard instead of a separate file, so the fit is self-contained and R is always consistent with the run that built the card (review comment WMass#6). - setupRabbit: after the input loop, extract R + N_gen once from the unfolding histmaker output and embed them in the fit input via rabbit add_auxiliary (the "scetlib_np" group). Presence-based lenient guard (response_matrix.has_response): embed only when an input carries both the response hist and the prefsr gen-total; raise if more than one qualifies. - response_matrix: add has_response() (cheap both-present check, never raises). - param_model: read R only from indata.auxiliary["scetlib_np"]; drop the unfolding_hdf5_path argument and the sigma_gen(lambda_c) N_gen fallback (raise if N_gen is absent -- the proxy made the central closure circular). - scetlib_np package __init__: import SCETlibNPParamModel lazily (PEP 562) so response_matrix can be imported by setupRabbit without pulling in TensorFlow. Requires a rabbit with TensorWriter.add_auxiliary (WMass/rabbit#145); the submodule bump is a follow-up. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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What
Adds a generic
auxiliarygroup to the input HDF5: a side channel forParamModels to carry pre-computed arrays inside the datacard rather thanreading them from a separately-versioned external file at fit time. It is not
used by the fit itself.
This mirrors the existing
external_termsmechanism (rabbit/external_likelihood.py):the
TensorWritercollects named bundles and serializes them inwrite();FitInputDataexposes them on read.Motivation
A downstream ParamModel (WRemnants
SCETlibNPParamModel) currently reads itsreco×gen response matrix
R(and the gen-totalN_gen) from a hand-made skimfile that has to be kept in sync with the histmaker output by hand — a staleness
risk. With this, the extraction can be run once at datacard-build time and the
finished arrays stored in the fit input, so
Ris always consistent with the runthat produced the datacard. The feature itself is fully generic —
"scetlib_np"is just one entry name.
API
TensorWriter.add_auxiliary(name, datasets)—datasetsis a dict{key: np.ndarray | list[str]}. Duplicate names are rejected.FitInputData.auxiliary—{name: {key: ndarray | list[str]}}; an empty dictwhen no group is present (back-compatible with existing datacards).
rabbit/auxiliary.py:write_auxiliary_group/read_auxiliary_from_h5.Round-trip guarantees
via
writeFlatInChunks(original_shapeattr) +maketensor.list[str]via a vlen-strdataset, like
external_terms'params.Testing
New
tests/test_auxiliary.py(4 cases, all pass):R,N_gen, axis-namelists, edges) round-tripped through
add_auxiliary → write() → FitInputData;{};add_auxiliaryrejects a duplicate bundle name;write_auxiliary_group/read_auxiliary_from_h5round-trip with mixeddtypes (float32, int64, >2D) and the
None → {}case.autoflake/isort/black/flake8clean.Notes
additive.
commit).
🤖 Generated with Claude Code