transform_funct_1d — ONED `function` op

• Data kinds: signal → pairs

• Call: import fullseye as fs; fs.ledger.transform_funct_1d(y, mult_x=1.0, add_x=0.0, mult_y=1.0, add_y=0.0) (to call the implementation directly, import funct1d; funct1d.transform_funct_1d(y, mult_x=1.0, add_x=0.0, mult_y=1.0, add_y=0.0); from the registry, ops1d.get("transform_funct_1d"))

Usage

Independent affine transform of x and y (HALCON `transform_funct_1d`).

Returns explicit pairs `(mult_x * i + add_x, mult_y * y[i] + add_y)` for

`i = 0..n-1. Note mult_x = 0` collapses the abscissa to a single

point (accepted; the result is then not a function of x).

:param y: 1-D function (may be empty).

:param mult_x, add_x, mult_y, add_y: finite affine coefficients.

:returns: `(n, 2) float64 array of (x, y)` pairs.

:raises ValueError: non-1-D / NaN / Inf input, or non-finite parameter.

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)

• (none yet)

Ops the type connects to (they accept `pairs` 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).*

transform_funct_1d — ONED function op