spectral op• Data kinds: signal → matrix
• Call: import fullseye as fs; fs.ledger.ula_snapshots(angles_deg=(10.0, 16.0), n_elements: 'int' = 8, n_snapshots: 'int' = 200, *, spacing: 'float' = 0.5, snr_db: 'float | None' = None, seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import mathspectral; mathspectral.ula_snapshots(angles_deg=(10.0, 16.0), n_elements: 'int' = 8, n_snapshots: 'int' = 200, *, spacing: 'float' = 0.5, snr_db: 'float | None' = None, seed: 'int' = 0) -> 'np.ndarray'; from the registry, opsmath.get("ula_snapshots"))
Generates the complex snapshots (M, K) received by a uniform linear array (an input whose direction-of-arrival answer is known in advance).
An independent complex Gaussian signal per source (uncorrelated). If snr_db is given, white noise is added per element (None = no noise).
Every mathops op validates its input before computing (nothing slips through silently):
• complex input raises ValueError — coercing to float64 silently discards the imaginary part (numpy only emits a ComplexWarning and returns a plausible-looking wrong real number). State .real/.imag/abs() explicitly, or use complexops, which handles complex data.
• masked arrays with masked elements raise ValueError — the implicit conversion that peels off the mask and uses the raw values underneath is refused. Say explicitly whether to fill or to drop.
• NaN/Inf raises ValueError on every input (refused with the count stated — it propagates through the whole result).
• Shapes are strict: 1-D and 2-D are never implicitly promoted or broadcast (a matrix in a vector slot, or a vector in a matrix slot, raises ValueError; reshape explicitly).
• Size cap: ops that take a matrix, and the stat_histogram bins, raise ValueError beyond mathops.MAX_ELEMENTS (2^26 ≈ 67 million elements).
• 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.
• spectral_tour — py -3.11 examples/spectral_tour.py
matrix as input)mat_solve · mat_lstsq · mat_svd · mat_eigh · mat_pinv · mat_cond · mat_lu · mat_qr
spectral)lomb_scargle · music_doa · esprit_doa · n_sources_mdl · hilbert_analytic
*Provenance: mathspectral.py — MATH 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.