# `aug_detector_rolloff` — Detector Roll Off Augmenter _Group_: **Augmentation** · _Binding_: `n4m.sklearn.DetectorRollOffAugmenter` · _C ABI_: `n4m_aug_detector_rolloff_*` ## Description Detector edge roll-off artifact. ### Parameters | Name | Type | Default | |------|------|---------| | `detector_model` | `int` | `4` | | `effect_strength` | `float` | `1.0` | | `noise_amplification` | `float` | `0.02` | | `include_baseline_distortion` | `bool` | `True` | | `wavelengths` | `—` | `None` | | `rng` | `Optional[PCG64]` | `None` | | `seed` | `int` | `0` | ## Explanations ### Bibliographic source _Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring._ ### Mathematical principle Detector edge roll-off artifact. ### Implementation C ABI `n4m_aug_detector_rolloff_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.DetectorRollOffAugmenter`. The same numerical kernel backs every language binding. ### Usage ```python from n4m.sklearn import DetectorRollOffAugmenter op = DetectorRollOffAugmenter() X_transformed = op.fit_transform(X) ``` ### Benchmarks Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). Only backends that implement this method are listed; libraries without the method are omitted. **Verdict**  ·  ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance  ·  ✓ bind = pls4all binding agrees with the C++ baseline  ·  ✗ divergent  ·  ⚠ error  ·  — not run. The fastest backend per column is marked 🏆. **Reference gate**: strict — numeric equivalence (`rmse_rel_tol ≤ 1e-12`). ::::{tab-set} :class: parity-tabs :::{tab-item} 1 thread :sync: threads-1
BackendParity50×250 (ms)250×50 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ ref
Python · external
nirs4allsource
::: :::: --- _See also_: [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)