apply_transport — COLORTRANSPORT `plan_use` op

• Data kinds: transport_plan × signal → signal

• Call: import fullseye as fs; fs.ledger.apply_transport(plan, target_values) (to call the implementation directly, import colortransport; colortransport.apply_transport(plan, target_values); from the registry, opscolortransport.get("apply_transport"))

Usage

Pull target_values back onto the source points through a transport plan (the barycentric map).

For each row (source), it returns the mean of destination values weighted by the mass carried. This is the entry point for "using the plan to fix a picture" -- if a plan is only made and has no use, the output of sinkhorn becomes a dead end in the ledger (the shape of "an entry point exists but no consumer op", which this repo has repeatedly stepped into).

A row with zero mass has no destination -- filling it with 0 would silently mix in "black pixels", so ValueError.

Background guides (the physics and conventions behind this op)

• colorimetry — Colorimetry and spectra — colour is fixed only by spectrum × illuminant × observer

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)

• color_transport — py -3.11 examples/color_transport.py

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

wasserstein_1d · transport_plan_1d · sinkhorn · sinkhorn_distance · sinkhorn_divergence

Same category (`plan_use`)

transport_cost

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

apply_transport — COLORTRANSPORT plan_use op