interp_poly op• Data kinds: signal × signal → signal
• Call: import fullseye as fs; fs.ledger.poly_eval(coeffs, x) (to call the implementation directly, import mathops; mathops.poly_eval(coeffs, x); from the registry, opsmath.get("poly_eval"))
Evaluate a polynomial (coefficients highest-power-first) at *x*.
*coeffs* is the 1-D array poly_fit returns in "coeffs" (or any hand-written one, [c_d, ..., c_1, c_0]); *x* is a finite scalar or 1-D array. A scalar returns a Python float, an array returns float64. Evaluation is by Horner's scheme (np.polyval) — numerically the right way to evaluate, though it cannot repair a badly-conditioned *fit* (see poly_fit's cond).
HALCON: no polynomial tuple operator (compose tuple_pow + arithmetic).
Raises ValueError when a finite input overflows float64 — a degree-*d* polynomial at |x| well above 1 grows like |x|**d, so mixing up the two arguments (a long signal used as coefficients) silently produced inf before this guard (chain fuzzer wave-7: 256 coefficients evaluated at |x|<=22 -> 22**255). An unusable inf must not flow downstream.
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
• signal_filter — py -3.11 examples/signal_filter.py
signal as input)mat_solve · mat_lstsq · stat_describe · stat_histogram · stat_zscore · interp_linear · interp_cubic · interp_scattered
interp_poly)interp_linear · interp_cubic · interp_scattered · poly_fit · 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.