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`).
> The detailed description below is the original text — the summary and the headings are translated.
校正証明書や規格が与えるのは「半幅 a」「95 % で ±U」のような形であって標準偏差
ではない。伝播則が食えるのは標準不確かさだけなので、ここで揃える:
矩形(一様) u = a / sqrt(3) 三角 u = a / sqrt(6)
U 字(逆正弦) u = a / sqrt(2) normal_95 u = a / 1.959964
★どれも分布の分散の定義から出る厳密な値で、モンテカルロで標本標準偏差を
取れば同じ数に収束する(`gum_monte_carlo` が独立に確かめる)。
★★既定の分布を置かない。一番よく使うからといって矩形を既定にすると、
形を書き忘れた成分が黙って a/sqrt(3) になる —— 三角のつもりなら 1.41 倍、
95 % 区間のつもりなら 1.13 倍ずれた不確かさが、例外を出さずに下流へ流れる。
• 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.