numerics op• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)
• Call: import fullseye as fs; fs.ledger.gauss_quadrature(n: 'int', kind: 'str' = 'legendre', a: 'float | None' = None, b: 'float | None' = None) -> 'dict' (to call the implementation directly, import mathnumerics; mathnumerics.gauss_quadrature(n: 'int', kind: 'str' = 'legendre', a: 'float | None' = None, b: 'float | None' = None) -> 'dict'; from the registry, opsmath.get("gauss_quadrature"))
Nodes and weights of n-point Gauss quadrature. ∫ f ≈ Σ w_k f(x_k).
With n points, integrates polynomials up to degree 2n−1 exactly (the equally spaced trapezoidal rule only up to degree 1). kind="legendre" can be mapped to an interval [a, b] (default [−1, 1]). Other kinds integrate including the weight function (see integral).
Gate: monomials of degree 2n−1 are exact, degree 2n are not (both directions).
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.
• numerics_tour — py -3.11 examples/numerics_tour.py
table as input)dynsys_poincare_section · dwt_inverse · chebyshev_eval_nd
numerics)erf · erfc · bessel · gauss_cubature · chebyshev_coeffs_nd · chebyshev_eval_nd · low_discrepancy · chebyshev_nodes
*Provenance: mathnumerics.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.