interp_poly op• Data kinds: signal × signal × signal → signal
• Call: import fullseye as fs; fs.ledger.interp_linear(x, y, xq, out_of_range='raise') (to call the implementation directly, import mathops; mathops.interp_linear(x, y, xq, out_of_range='raise'); from the registry, opsmath.get("interp_linear"))
Piecewise-linear interpolation of (x, y) samples at query *xq*.
*x* must be strictly increasing (fail-closed: an unsorted or duplicated grid raises rather than being silently reordered). *xq* is a scalar or a 1-D array; a scalar query returns a Python float, an array returns float64.
Out-of-range is an explicit choice, never silent: 'raise' (default) refuses any query outside [x[0], x[-1]] — a calibration table queried beyond its calibrated range is a wrong answer waiting to happen — while 'clamp' holds the boundary values (the honest flat extension; there is deliberately no silent linear extrapolation mode).
Exact on the nodes and exact for data that is genuinely piecewise linear.
HALCON: get_y_value_funct_1d interpolates function pairs the same way (see funct1d, which works HALCON's index-grid convention; this op takes an arbitrary strictly-increasing x grid).
Every mathops op validates its input before computing (nothing slips through silently):
• complex input raises ValueError — coercing to float64 silently discards the imaginary part (numpy only emits a ComplexWarning and returns a plausible-looking wrong real number). State .real/.imag/abs() explicitly, or use complexops, which handles complex data.
• masked arrays with masked elements raise ValueError — the implicit conversion that peels off the mask and uses the raw values underneath is refused. Say explicitly whether to fill or to drop.
• NaN/Inf raises ValueError on every input (refused with the count stated — it propagates through the whole result).
• Shapes are strict: 1-D and 2-D are never implicitly promoted or broadcast (a matrix in a vector slot, or a vector in a matrix slot, raises ValueError; reshape explicitly).
• Size cap: ops that take a matrix, and the stat_histogram bins, raise ValueError beyond mathops.MAX_ELEMENTS (2^26 ≈ 67 million elements).
• 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.
• math_metrology — py -3.11 examples/math_metrology.py
• poc_thermal_radiometry — py -3.11 examples/poc_thermal_radiometry.py
signal as input)mat_solve · mat_lstsq · stat_describe · stat_histogram · stat_zscore · interp_cubic · interp_scattered · poly_fit
interp_poly)interp_cubic · interp_scattered · poly_fit · poly_eval · poly_roots
*Provenance: mathops.py — MATH 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.