pairs_to_signal — REPRCONV `pairs` op

• Data kinds: pairs → signal

• Call: import fullseye as fs; fs.ledger.pairs_to_signal(pairs) (to call the implementation directly, import reprconv; reprconv.pairs_to_signal(pairs); from the registry, opsreprconv.get("pairs_to_signal"))

Usage

Pairs (N,2) → the dependent variable as a signal (N,). The second exit from pairs.

Extracts only column 1 (y). funct_1d_to_pairs returns (N,2) with (x, y) side by side, so this is its reverse direction -- if x is evenly spaced, signal -> pairs -> signal is bit-identical. Passing pairs whose x is not evenly spaced silently drops the information in x (signal is a type that assumes evenly spaced indices). That is why pairs_to_table always outputs x_uniform alongside.

Args:

pairs: (N, 2) or a tuple of 2 equal-length 1-D arrays.

Returns:

(N,) float64.

Raises:

ValueError: invalid shape / non-finite.

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)

• representation_conversion — py -3.11 examples/representation_conversion.py

• representation_roundtrip — py -3.11 examples/representation_roundtrip.py

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

—

Same category (`pairs`)

pairs_to_image2d · pairs_to_table

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

pairs_to_signal — REPRCONV pairs op