pp_derivate — Order-\(d\) finite-difference derivative¶
Group: Preprocessing · C ABI: n4m_transform_derivative_*
Description¶
Finite-difference derivative along the wavelength axis.
Parameters¶
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API and bindings¶
C ABI (ABI 2): n4m_transform_derivative_create · n4m_transform_derivative_destroy · n4m_transform_derivative_fit · n4m_transform_derivative_output_cols · n4m_transform_derivative_transform. Use the linked public header for the exact signature, configuration, and result handles.
Python (verified public re-export):
from n4m.transform.smoothing import Derivate
Source signature: Derivate(order: int = 1, delta: float = 1.0).
R: no current source-verified entry point was found for this catalog method.
MATLAB / Octave: no current source-verified entry point was found for this catalog method.
Explanations¶
Bibliographic source¶
No unique canonical paper: this is repeated forward finite differencing. For the spectroscopic rationale see Norris & Williams (1984), Optimization of mathematical treatments of raw near-infrared signal.
Mathematical principle¶
The operator applies \(d\) successive first differences along wavelength and divides by delta at each pass, reducing the output width by order. A first difference removes constant offsets; a second also suppresses linear trends.
Appropriate uses¶
Emphasizing slopes or narrow bands when a reduced-width difference representation is acceptable.
Limits and validation¶
Differencing amplifies high-frequency noise, shortens the feature axis, and assumes uniform spacing represented by delta. It is distinct from the shape-preserving gradient operators.
Implementation¶
n4m.transform.smoothing.Derivate calls n4m_transform_derivative_*; repeated difference kernels are in preprocessing/derivatives/derivate.c.
The ABI-2 implementation is the n4m_transform_derivative_* lifecycle in libn4m.
Sources and provenance¶
See also: methods index · interactive dashboard