# `pp_first_derivative` — First derivative _Group_: **Preprocessing** · _Binding_: `n4m.sklearn.FirstDerivative` · _C ABI_: `n4m_pp_first_derivative_*` ## Description ``np.gradient(X, delta, axis=1, edge_order=...)`` (shape-preserving). ### Parameters | Name | Type | Default | |------|------|---------| | `delta` | `float` | `1.0` | | `edge_order` | `int` | `2` | ## Explanations ### Bibliographic source Standard finite-difference / gap derivative; see Savitzky & Golay (1964) and Norris & Williams (1984). ### Mathematical principle Approximates $\mathrm{d}\mathbf{x}/\mathrm{d}\lambda$ by finite differences. The first derivative removes constant baseline offsets (additive scatter) and accentuates inflection points of overlapping bands. ### Implementation C ABI `n4m_pp_first_derivative_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.FirstDerivative`. The same numerical kernel backs every language binding. ### Usage ```python from n4m.sklearn import FirstDerivative op = FirstDerivative() 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
| Backend | Parity | 50×250 (ms) | 250×50 (ms) |
|---|---|---|---|
| C++ native · libn4m | |||
pls4all.cpp.blas+omp | ✓ ref | — | — |
| Python · external | |||
ref.python_numpy | source | — | — |