sensitivity op• Data kinds: design × signal → table
• Call: import fullseye as fs; fs.ledger.sens_morris(design=None, y=None, negligible=0.05) -> 'dict' (implementation को सीधे call करना हो तो import sensitivity; sensitivity.sens_morris(design=None, y=None, negligible=0.05) -> 'dict', registry से लेना हो तो opsmultivar.get("sens_morris"))
Morris elementary effects -> हर factor के mu, mu*, sigma (Morris 1991; mu* Campolongo et al. 2007)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
design must come from doe_morris (its trajectory bookkeeping is used) and y holds the model output for its rows in order. For trajectory t and step s that moves factor i by ±delta: EE_i = (y_s - y_{s-1}) / (±delta) in unit-cube units, and also per natural unit of the factor (ee_natural; on a log-scale factor the step is in log10 units).
mu (mean EE) can cancel when effects change sign; mu_star (mean |EE|) does not and is the ranking measure; sigma (standard deviation, ddof = 1) is large when the effect depends on where the other factors are (non-linear or interacting). class per factor: negligible (mu* below negligible x the largest mu*), linear/additive (sigma < 0.1 mu*: the effect is the same everywhere — a convention of this op, stated), otherwise non-linear or interacting.
• design_of_experiments family guide
• 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।
• sensitivity_tour — py -3.11 examples/sensitivity_tour.py
table को input के रूप में लेते हैं)—
sensitivity)doe_morris · doe_saltelli · sens_sobol
*Provenance: sensitivity.py — MULTIVAR 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.