assess op• Data kinds: images → table
• Call: import fullseye as fs; fs.ledger.piv_time_statistics(images, window=32, overlap=0.5, **kw) (to call the implementation directly, import pivops; pivops.piv_time_statistics(images, window=32, overlap=0.5, **kw); from the registry, opspiv.get("piv_time_statistics"))
Image sequence → time average, RMS of the fluctuations and Reynolds stresses.
Displacement is measured for each consecutive pair, and statistics along time are taken. These 3 are the starting point for describing turbulence; the sign and size of u'v' are momentum transport itself.
Only 1 pair is meaningless (fluctuations cannot be defined), so 3 or more images are required.
Args:
images: sequence of 3 or more images.
window / overlap / kw: passed to piv_cross_correlate.
Returns:
dict: mean ((2, h, w)), rms (same), reynolds
(<u'v'>, (h, w)), turbulence_intensity, n_pairs,
rows / cols / step.
• piv_displacement 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.
• piv_field_analysis_tour — py -3.11 examples/piv_field_analysis_tour.py
table as input)—
assess)piv_sample_at_windows · piv_error_stats · piv_peak_locking
*Provenance: pivops.py — PIV 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.