distance_funct_1d — ONED `function` op

• Data kinds: signal × signal → measurement

• Call: import fullseye as fs; fs.ledger.distance_funct_1d(y1, y2, mode='max') -> 'float' (to call the implementation directly, import funct1d; funct1d.distance_funct_1d(y1, y2, mode='max') -> 'float'; from the registry, ops1d.get("distance_funct_1d"))

Usage

Distance between two functions on the same grid (HALCON `distance_funct_1d`).

`mode="max" is the Chebyshev / sup distance max |y1 - y2|`;

`mode="mean"` the mean absolute difference. Both are symmetric and zero

iff the functions are identical.

:param y1: 1-D function, at least 1 sample.

:param y2: 1-D function, same length as *y1* (unequal lengths raise —

pointwise distance is undefined across different grids; silent numpy

broadcasting is explicitly rejected).

:param mode: `"max" or "mean"` (anything else raises).

:raises ValueError: non-1-D / NaN / Inf input, empty input, length mismatch,

or unknown *mode*.

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• signal_funct1d — py -3.11 examples/signal_funct1d.py

Ops the type connects to (they accept `measurement` as input)

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Same category (`function`)

create_funct_1d_array · create_funct_1d_pairs · smooth_funct_1d_gauss · smooth_funct_1d_mean · derivate_funct_1d · integrate_funct_1d · zero_crossings_funct_1d · local_min_max_funct_1d

*Provenance: funct1d.py — ONED operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

distance_funct_1d — ONED function op