photon_statistics — PHOTON `counting` op

• 데이터 종류: image2d → table

• 호출: import photoncount; photoncount.photon_statistics(counts)(또는 opsphoton.get("photon_statistics"))

광자 계수 프레임의 푸아송 통계: 정말로 샷 노이즈 한계인가?

> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.

Returns a dict: `mean · variance (population, ddof=0`) ·

`fano_factor = variance / mean` (1 for a Poisson process) ·

`snr_poisson = sqrt(mean)` (the theoretical photon-limited SNR) ·

`snr_measured = mean / std` (what this frame actually achieved) ·

`total_counts · n_samples · zero_fraction` (the fraction of pixels

that saw no photon at all — the honest measure of "photon starved";

`exp(-lambda) for a flat field) · max_counts`.

The Fano factor is evidence of Poisson statistics only on a flat field.

On a structured scene the scene's own spatial variance dominates and the

ratio is large and meaningless — this op computes the number, it cannot tell

you which situation you are in. Measured on the test scenes: a flat

`lambda = 100` field (512x512, seed 0) gives 1.001089; the same detector

looking at a linear ramp from 20 to 180 photons gives 22.4102. Both are

"correct" and only one of them means anything.

Raises `ValueError`: negative, non-finite or non-2-D *counts*, fewer

than 2 pixels (no variance), an all-zero frame (`fano_factor` would be

`0/0` — say "no photons were detected" instead of returning NaN), and a

frame with exactly zero variance (`snr_measured would be inf`; for

`n >= 2` a constant frame is not a Poisson realisation but a synthetic

constant, i.e. an input mistake).

• 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

—

*Provenance: photoncount.py — PHOTON 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*

photon_statistics — PHOTON counting op