trials_for_all_success — SPC `pod` op

• Data kinds: none → scalar (an op determined by its arguments alone — it takes no image or data input)

• Call: import fullseye as fs; fs.ledger.trials_for_all_success(p_min, *, confidence=0.95) (to call the implementation directly, import pod; pod.trials_for_all_success(p_min, *, confidence=0.95); from the registry, opsspc.get("trials_for_all_success"))

• GPU: none (CPU reference implementation only)

Usage

Return the minimum trial count needed for all successes to establish p >= p_min at one-sided confidence (scalar).

> The detailed description below is the original text — the summary and the headings are translated.

返り値は整数で、Clopper--Pearson の全数成功時の閉形式 (1-confidence)**(1/n) >= p_min に基づく。たとえば POD >= 0.90 を 95 % 信頼で示すには 29/29 が必要である。誤警報率 p <= p_max を 0 件で示す件数は trials_for_all_success(1-p_max, confidence=...) と同じ。

拒否(ValueError): p_min または confidence が有限な (0, 1) 外。

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)

• proportion_interval_audit — py -3.11 examples/proportion_interval_audit.py

Ops the type connects to (they accept `scalar` as input)

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Same category (`pod`)

proportion_interval · pod_ahat_fit · pod_from_ahat · pod_hitmiss_fit · pod_false_call · pod_decision_tradeoff

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

trials_for_all_success — SPC pod op