piv_multipass — PIV `estimate` op

• Data kinds: image2d × image2d → flow2d

• Call: import pivops; pivops.piv_multipass(a, b, windows=(64, 32), overlap=0.5, peak='gauss3', window_func='hann', outlier_threshold=2.0, normalize='overlap', search_limit=0.25) (or opspiv.get("piv_multipass"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

粗い窓から細かい窓へ段を下げる多段 PIV。返りは `(flow, info)`。

各段の結果を次の段の予測変位として使う(整数量だけ 2 枚目の窓をずらす)。

段の間で外れ値を除いて埋めるのは意図的 —— 外れたベクトルをそのまま予測に

使うと、その窓は次の段でも外れたまま固定される。

Args:

a, b: 画像対。

windows: 大きい順の窓列。各段は前段の倍数関係でなくてよいが、

格子が変わるので予測は最近傍で載せ替える。

overlap / peak / window_func: :func:piv_cross_correlate と同じ。

outlier_threshold: 段間の正規化中央値検定の閾値。`None` で無効。

Returns:

最終段の `(flow, info)。info["passes"]` に各段の窓の大きさ。

Detailed usage guide

• piv_displacement family guide

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)

• piv_flow_from_particles — py -3.11 examples/piv_flow_from_particles.py

• poc_strain_history — py -3.11 examples/poc_strain_history.py

Ops the type connects to (they accept `flow2d` as input)

piv_deform_pass · piv_outlier_mask · piv_replace_outliers · piv_vorticity · piv_divergence · piv_flow_magnitude · piv_to_velocity · piv_velocity_gradient

Same category (`estimate`)

piv_cross_correlate · piv_deform_pass · piv_ensemble_correlate

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

piv_multipass — PIV estimate op