change op• Data kinds: signal → table
• Call: import fullseye as fs; fs.ledger.spc_cusum(signal, target, k=0.5, h=5.0) (to call the implementation directly, import spc; spc.spc_cusum(signal, target, k=0.5, h=5.0); from the registry, opsspc.get("spc_cusum"))
Tabular CUSUM chart for individual measurements.
signal is a 1-D series of individual measurements. With a reference value target, a slack k (in the same units as the measurements — commonly half the shift you want to catch) and a decision interval h::
C+_i = max(0, C+_{i-1} + (x_i - target) - k)
C-_i = max(0, C-_{i-1} - (x_i - target) - k)
starting from C+_0 = C-_0 = 0, alarming at index i when C+_i > h or C-_i > h. Returns a dict with the c_plus / c_minus arrays, the integer alarms indices, the first alarm index (or -1), and the echoed target / k / h.
Ground truth (pinned in the tests): a series constant at target keeps C+ == C- == 0; after a sustained upward step of size d > k the upper sum grows at exactly d - k per sample.
Raises ValueError: a non-1-D / empty *signal*, a non-finite *target* / *k* / *h*, a negative *k*, or a non-positive *h*.
• 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_spc — py -3.11 examples/poc_spc.py
table as input)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
change)*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.