scene op• Data kinds: table → pairs
• Call: import fullseye as fs; fs.ledger.light_wavelengths(light: 'dict', samples: 'int' = 5) (to call the implementation directly, import optscene; optscene.light_wavelengths(light: 'dict', samples: 'int' = 5); from the registry, opsoptics.get("light_wavelengths"))
Reduce a source's spectrum to the few wavelengths and weights used for imaging.
Laser = 1 line, LED = a few narrowband lines, halogen = a few broadband lines. Even on the same surface, the split between specular and scattered changes with wavelength, so a broadband source is correctly imaged by superposing them.
Procedure: light["wavelength_nm"] (550 if absent) is the centre λ, light["bandwidth_nm"] (0 if absent) is the full width bw, and k = max(samples, 1) lines are placed equally spaced over λ ± bw/2. The weights are Gaussian exp(-0.5 (2x)^2) (x is the position normalized to [-1, 1], the ends being 2σ), normalized so they sum to 1. If bw <= 0 (laser) or k == 1, a single λ with weight 1.
• light: the result of light_spec (a dict). Missing keys fall back to default values (if it is not a dict, ValueError; this stops a row list of sort table from flowing in).
• samples: number of lines. Default 5. Values below 1 are rounded to 1. An odd number includes the centre wavelength.
• Return value: (wavelengths (k,), weights (k,)), 2 float64 arrays. An approximation that does not model the real spectral shape (LED asymmetry, the halogen blackbody distribution).
layout_capture calls this with spectral_samples lines and sums the per-wavelength results with these weights.
Every optics op validates its input before computing (nothing slips through silently):
• Units are baked into the argument name — _mm / _um / _deg / _mrad. Confusing mm with µm does not crash; it yields a plausible-looking wrong answer, so the name prevents it. Nothing here guesses the unit from the magnitude.
• Strings raise ValueError — float('50') succeeds, so an unparsed configuration value would slip through as a length (measured: thin_lens('50', '200') returned a plausible 66.667 mm). bool is refused too, as the implicit promotion True == 1.
• complex / masked arrays raise ValueError (real-valued slots only; silently dropping the imaginary part or peeling off the mask is refused). NaN/Inf raises ValueError on every input.
• Division by zero and its relatives are refused by name: focal length 0, radius of curvature 0, refractive index <= 0, a fully opaque aperture (all zeros, so the normalisation is 0/0), a PSF whose sum is <= 0, a Stokes vector with S0 = 0, and an object sitting at the front focal point (the image is at infinity).
• Only two ops return a non-finite value, and both state it as a contract: depth_of_field returns far_mm = inf beyond the hyperfocal distance (that is what the hyperfocal distance means), and gaussian_beam returns wavefront_radius_mm = inf at the waist (the radius of curvature of a plane wavefront). Both also return a finite companion (far_is_infinite / curvature_per_mm). Any other silent NaN/Inf is detected internally and raises ValueError — "float64 overflowed" and "the answer is infinite" are different claims, so the first is never returned wearing the face of the second.
• Size caps: generated grids are capped by optics.MAX_GRID (4096); supplied fields/PSFs/apertures by optics.MAX_FIELD_ELEMENTS (2^24); ABCD element chains by optics.MAX_SYSTEM_ELEMENTS (1024); Zernike by MAX_ZERNIKE_TERMS (512) / MAX_ZERNIKE_ORDER (40) / MAX_ZERNIKE_BASIS (2^25). This closes, fail-closed, the paths where a small argument triggers a huge internal allocation (measured: n_max=40 × 4096² needs 108 GB).
• Physically impossible states are refused too: a Stokes vector with degree of polarisation > 1, negative transmittance, negative intensity, and invalid Zernike indices such as n-|m| odd.
• dataset_conventions — Training-dataset conventions — COCO / YOLO / VOC and pitfalls in visual inspection
• mv_cameras — Industrial camera makers (sensor mapping, line scan / TDI)
• mv_illumination_practice — Lighting in practice — wavelength, polarisation, drive modes, ambient light, safety
• mv_image_sensors — Industrial image sensors (mainly current products)
• virtual_machine_vision — Virtual machine vision — enumerating the parameters and an object model
• 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.
• vision_layout_from_catalog — py -3.11 examples/vision_layout_from_catalog.py
pairs as input)scene)scene_material · scene_plane · scene_sphere · scene_box · scene_cylinder · surface_defect · surface_finish · random_defects
*Provenance: optscene.py — OPTICS 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.