shape_from_polarization op• Data kinds: image2d × image2d → table
• Call: import fullseye as fs; fs.ledger.sfp_estimate_index(dolp, zenith_deg, model='specular', mask=None, n_bounds=(1.05, 3.0)) (to call the implementation directly, import sfpolar; sfpolar.sfp_estimate_index(dolp, zenith_deg, model='specular', mask=None, n_bounds=(1.05, 3.0)); from the registry, opsoptics.get("sfp_estimate_index"))
Estimate the refractive index from DoLP observed at known zenith angles — a least-squares fit of the Fresnel curve, plus Brewster's n = tan(theta_B) at the DoLP maximum for the specular model.
*dolp* and *zenith_deg* are maps of the same shape (the zenith from a coarse geometry such as a sphere's silhouette, a CAD model or a coarse depth). The fit minimises sum (rho_model(theta; n) - dolp)^2 over *mask* with a coarse grid over *n_bounds* followed by a bounded scalar minimisation.
Returns a dict: refractive_index (the fit), rmse (DoLP units), n_pixels, model, and for the specular model brewster_index = tan of the zenith where DoLP is largest (with brewster_dolp, the DoLP there — far below 1 means the data never reached Brewster's angle and the number is a lower bound, not an estimate).
The DoLP maximum by itself says nothing about *n* (the specular maximum is exactly 1 for every index); the index is in where the maximum falls, which is why a zenith map is required.
Raises ValueError: shapes differ; DoLP outside [0, 1] or zenith outside [0, 90]; fewer than 3 pixels in the mask; bounds not 1 < lo < hi; unknown *model*.
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.
• 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.
• poc_shape_from_polarization — py -3.11 examples/poc_shape_from_polarization.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
shape_from_polarization)sfp_dolp_model · dolp_to_zenith · aolp_to_azimuth · sfp_render · sfp_normals · sfp_disambiguate · sfp_height
*Provenance: sfpolar.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.