function op• Data kinds: signal → signal
• Call: import fullseye as fs; fs.ledger.derivate_funct_1d(y) (to call the implementation directly, import funct1d; funct1d.derivate_funct_1d(y); from the registry, ops1d.get("derivate_funct_1d"))
First derivative by central differences (HALCON `derivate_funct_1d`).
Units are y per sample (the x-grid is the index): for a physical signal
sampled every `dt seconds, divide the result by dt`. Interior points
use the second-order central difference; the two boundary points use one-sided
differences (`numpy.gradient`).
:param y: 1-D function, at least 2 samples (a derivative needs a neighbour).
:returns: float64 array of the same length.
:raises ValueError: non-1-D / NaN / Inf input, or fewer than 2 samples.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_tree_ring_dendro — py -3.11 examples/poc_tree_ring_dendro.py
• poc_veiling_glare — py -3.11 examples/poc_veiling_glare.py
• signal_funct1d — py -3.11 examples/signal_funct1d.py
signal as input)create_funct_1d_array · create_funct_1d_pairs · smooth_funct_1d_gauss · smooth_funct_1d_mean · integrate_funct_1d · zero_crossings_funct_1d · local_min_max_funct_1d · abs_funct_1d
function)create_funct_1d_array · create_funct_1d_pairs · smooth_funct_1d_gauss · smooth_funct_1d_mean · integrate_funct_1d · zero_crossings_funct_1d · local_min_max_funct_1d · abs_funct_1d
*Provenance: funct1d.py — ONED operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.