uncertainty op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.gum_standard_uncertainty(table, halfwidth='halfwidth', distribution='distribution') (to call the implementation directly, import spc; spc.gum_standard_uncertainty(table, halfwidth='halfwidth', distribution='distribution'); from the registry, opsspc.get("gum_standard_uncertainty"))
Convert uncertainty components from the shape of their distribution into standard uncertainties (table).
What a calibration certificate or standard gives is a form like "half-width a" or "±U at 95 %", not a standard deviation. The propagation law can only consume standard uncertainties, so they are aligned here:
rectangular (uniform) u = a / sqrt(3) triangular u = a / sqrt(6)
U-shaped (arcsine) u = a / sqrt(2) normal_95 u = a / 1.959964
★ Each is an exact value following from the definition of the distribution's variance; taking the sample standard deviation by Monte Carlo converges to the same number (gum_monte_carlo checks it independently).
★★ No default distribution is set. Making rectangular the default because it is used most often would mean a component whose shape was forgotten silently becomes a/sqrt(3) -- if triangular was meant it is off by 1.41x, if a 95 % interval was meant by 1.13x, and that uncertainty flows downstream without an exception.
• 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_measurement_system_analysis — py -3.11 examples/poc_measurement_system_analysis.py
table as input)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_propagate · gum_expanded · gum_monte_carlo · gum_validate
uncertainty)gum_propagate · gum_expanded · gum_monte_carlo · gum_validate
*Provenance: spc.py — SPC 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.