derivate_funct_1d — ONED function op

• データ種: signal → signal

• 呼び出し: import fullseye as fs; fs.ledger.derivate_funct_1d(y) (実装を直接呼ぶなら import funct1d; funct1d.derivate_funct_1d(y)、台帳から引くなら ops1d.get("derivate_funct_1d"))

使い方

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.

参考(サンプルデータ・文献)

• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。

• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。

• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• 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

型が繋がる次の op(signal を入力に取れる)

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

同カテゴリ(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. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.