reflect_rays — OPTICS scene op

• Data kinds: points × points → points

• Call: import fullseye as fs; fs.ledger.reflect_rays(directions, normals) (to call the implementation directly, import optscene; optscene.reflect_rays(directions, normals); from the registry, opsoptics.get("reflect_rays"))

Usage

Specular reflection d − 2(d·n)n. The partner of glassmirror.refract_rays (the reflecting side was missing).

Both incident directions and normals are (..., 3). Independent of the orientation (front/back) of the normals. Returns unit vectors.

Procedure: normalize directions and normals each, then take r = d - 2 (d·n) n and normalize once more. Replacing n with -n does not change (d·n) n, so trace_rays(...)["normal"] can be passed as is without worrying about the sign of the normals.

• directions, normals: last axis 3, finite. If the shapes differ, they broadcast by numpy's rules (1 normal for all rays, etc.).

• Failure (ValueError): last axis not 3 / non-finite / contains vectors of length 0 (mixing in the 0 normals of rays that did not hit makes it fail ―― filter with index >= 0 first).

• Returns: float64 unit vectors in the broadcast shape.

trace_rays → reflect_rays → trace_rays follows the 2nd-order specular reflection. For the refraction side use refract_rays.

Family-wide input contract (fail-closed)

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.

Detailed usage guide

• optics_imaging family guide

Background guides (the physics and conventions behind this op)

• 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

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)

• studio_raytrace_scene — py -3.11 examples/studio_raytrace_scene.py

Ops the type connects to (they accept points as input)

refract_rays · trace_rays · illumination_visibility · env_studio · env_lightbox

Same category (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.