function op• Data kinds: signal → pairs
• Call: import fullseye as fs; fs.ledger.invert_funct_1d(y) (to call the implementation directly, import funct1d; funct1d.invert_funct_1d(y); from the registry, ops1d.get("invert_funct_1d"))
• Return value through the ledger: fullseye.ledger.invert_funct_1d(...) returns **only the declared out type pairs** (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.invert_funct_1d.raw(...) or call funct1d.invert_funct_1d directly.
Swap the roles of x and y: `x = f^-1(y) (HALCON invert_funct_1d`).
Returns `{"x": y-values sorted ascending, "y": their original indices}`,
i.e. the (y, x) pairs ordered so the new abscissa is monotonic — ready for
`numpy.interp`-style lookup.
Honest limitation: this is a true inverse only when *y* is monotonic. A
non-monotonic function is multi-valued; the sort interleaves its branches
instead of resolving them (ties keep index order — numpy stable-ish
argsort). An empty input returns two empty arrays.
:param y: 1-D function (may be empty).
:returns: dict `{"x": float64 array, "y": float64 array}`.
:raises ValueError: non-1-D / NaN / Inf input.
• 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_veiling_glare — py -3.11 examples/poc_veiling_glare.py
pairs as input)—
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.