counting op• 데이터 종류: image2d → image2d
• 호출: import fullseye as fs; fs.ledger.photon_uncertainty(counts, relative=False, zero_floor=0.0)(구현을 직접 호출하려면 import photoncount; photoncount.photon_uncertainty(counts, relative=False, zero_floor=0.0), 원장에서 가져오려면 opsphoton.get("photon_uncertainty"))
광자 계수 프레임의 화소별 푸아송 오차 막대.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
For a Poisson variable the variance *equals* the mean, so the one-sigma uncertainty of a single measurement N is sqrt(N) — no calibration, no noise model to fit. With relative=True the returned map is the relative uncertainty 1/sqrt(N) instead (its reciprocal is the per-pixel SNR).
*zero_floor* replaces counts below it before the square root. It exists because N = 0 gives sqrt(0) = 0, i.e. "this pixel is exactly zero with no uncertainty", which is wrong: the 95% Poisson upper limit for a single observed zero is about 3 photons. Set zero_floor=1.0 for the common "one-count prior" convention. It is not applied silently — the default is 0.0 and the absolute map really does return 0 there.
Returns a float64 (H, W) image.
Raises ValueError: negative, non-finite or non-2-D *counts*, a negative *zero_floor*, and — instead of returning inf — relative=True with any pixel at 0 after the floor (that is the division 1/sqrt(0); pass zero_floor > 0 to say what a zero should mean).
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_thermal_radiometry — py -3.11 examples/poc_thermal_radiometry.py
image2d 를 입력으로 받는 것)photon_sample · photon_statistics · anscombe_transform · anscombe_inverse
counting)photon_sample · photon_statistics
*Provenance: photoncount.py — PHOTON 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.