spc_capability — SPC `capability` op

• Data kinds: signal → table

• Call: import fullseye as fs; fs.ledger.spc_capability(signal, lsl, usl, sigma=None) (to call the implementation directly, import spc; spc.spc_capability(signal, lsl, usl, sigma=None); from the registry, opsspc.get("spc_capability"))

Usage

Process capability indices Cp and Cpk from measurements and spec limits.

signal is a 1-D series of individual measurements; lsl / usl are the lower / upper specification limits (lsl < usl). With the sample mean mu and standard deviation sigma (sample std, ddof=1, unless one is passed in explicitly)::

Cp = (USL - LSL) / (6 sigma)

Cpk = min(USL - mu, mu - LSL) / (3 sigma)

Returns a dict with mean / std / cp / cpk and the echoed spec limits and midpoint. Cpk <= Cp always, with equality exactly when the process is centred (mu at the spec midpoint) — pinned in the tests.

Raises ValueError: a non-1-D / empty *signal*, fewer than two points (no spread to estimate), lsl >= usl, non-finite limits, or a non-positive standard deviation (a constant series has no capability to report).

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• poc_spc — py -3.11 examples/poc_spc.py

Ops the type connects to (they accept `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

Same category (`capability`)

—

*Provenance: spc.py — SPC operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

spc_capability — SPC capability op