scene op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.interface_budget(sensor: 'dict', interface: 'str' = 'CXP-12', links: 'int' = 4, efficiency: 'float' = 0.85, line_scan: 'bool' = False) -> 'dict' (to call the implementation directly, import optscene; optscene.interface_budget(sensor: 'dict', interface: 'str' = 'CXP-12', links: 'int' = 4, efficiency: 'float' = 0.85, line_scan: 'bool' = False) -> 'dict'; from the registry, opsoptics.get("interface_budget"))
• GPU: none (CPU reference implementation only)
Return the bandwidth-limited maximum frame / line rate from the link's transfer bandwidth.
At high resolution, high speed and with line scanning, it is quite common for the bottleneck to be not the sensor but the transmission bandwidth. An op for knowing which one is the bottleneck before shooting (it pairs with the optical optical_budget as its counterpart).
efficiency is the effective efficiency of encoding and packets (roughly 0.8-0.9 for CoaXPress). With line_scan=True the calculation is per 1 line (width x 1 pixel), and the line rate [kHz] is returned.
Return value dict: gbps (total bandwidth) / bytes_per_frame / max_fps / max_line_rate_khz (when line_scan) / interface / links.
Worked example: interface="CXP-12" with 4 links gives a raw bandwidth of 50 Gbit/s = 6.25 GB/s. With the default efficiency=0.85 the effective bandwidth is 42.5 Gbit/s = 5.3125 GB/s, so an 8 bit, 16k line sensor is capped at about 332 kHz by the bandwidth.
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. Exception, declared: the u_map of bottle_fit_from_pattern takes pixels that could not be measured as NaN (infinity is still refused).
• 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. Exception, declared: bottle_background_map returns NaN in u_mm for pixels whose ray is unusable (total internal reflection and the like), together with a valid mask and the counts.
• 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
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
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.