change op• Data kinds: signal → table
• Call: import fullseye as fs; fs.ledger.spc_ewma(signal, target, lam=0.2, L=3.0, sigma=None) (implementation को सीधे call करना हो तो import spc; spc.spc_ewma(signal, target, lam=0.2, L=3.0, sigma=None), registry से लेना हो तो opsspc.get("spc_ewma"))
individual measurements के लिए EWMA control chart (Roberts 1959)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
signal is a 1-D series of individual measurements. With a reference value target (the in-control mean), a smoothing constant lam in (0, 1] and a control-limit width L (in sigmas), the exponentially weighted moving average and its time-varying limits are::
z_i = lam * x_i + (1 - lam) * z_{i-1}, z_0 = target
var_i = sigma^2 * (lam / (2 - lam)) * (1 - (1 - lam) ** (2 (i + 1)))
UCL_i / LCL_i = target +/- L * sqrt(var_i)
sigma is the process standard deviation; if None it is estimated from the series as the sample std (ddof=1). The limits widen from the first sample to the asymptote target +/- L * sigma * sqrt(lam / (2 - lam)). EWMA, like CUSUM, catches small sustained shifts that a single-point Shewhart chart misses; lam trades memory (small = long memory, sensitive to small shifts) against speed.
Returns a dict with the z / ucl / lcl arrays, the integer alarms indices (z_i outside its limits), the first alarm index (or -1), the asymptotic ucl_inf / lcl_inf, and the echoed target / lam / L / sigma / in_control.
Ground truth (pinned in the tests): a series constant at target keeps z == target with no alarm; z is exactly the recursion above; ucl increases monotonically toward ucl_inf; with lam = 1 the chart reduces to a Shewhart individuals chart (z == x, limits constant at target +/- L sigma).
Raises ValueError: a non-1-D / empty *signal*, a non-finite *target* / *lam* / *L*, lam outside (0, 1], a non-positive *L*, a non-finite or non-positive *sigma*, or (when estimating) a constant series whose sample std is zero.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• poc_spc — py -3.11 examples/poc_spc.py
table को input के रूप में लेते हैं)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. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.