counting op• Data kinds: image2d → table
• Call: import photoncount; photoncount.photon_statistics(counts) (or opsphoton.get("photon_statistics"))
Poisson statistics of a photon-count frame: is it really shot-noise limited?
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).
• photon_timeresolved family guide
• 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.
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
table as input)—
counting)photon_sample · photon_uncertainty
*Provenance: photoncount.py — PHOTON operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.