lensmerit op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.merit_default(system=None, kind: 'str' = 'spot', fields=None, wavelengths=None, rings: 'int' = 4, reference: 'str' = 'chief', efl_target=None, efl_weight=None, field_weights=None, wavelength_weights=None, pupil_fill: 'float' = 0.98) -> 'dict' (to call the implementation directly, import lensmerit; lensmerit.merit_default(system=None, kind: 'str' = 'spot', fields=None, wavelengths=None, rings: 'int' = 4, reference: 'str' = 'chief', efl_target=None, efl_weight=None, field_weights=None, wavelength_weights=None, pupil_fill: 'float' = 0.98) -> 'dict'; from the registry, opsoptics.get("merit_default"))
Build the default merit function as an operand table: whole-pupil ray rows plus the EFL hold (table).
One group row per field and wavelength: kind="spot" is TRAN (transverse ray aberrations, mm), kind="wavefront" is OPDW (OPD, waves); *reference* "chief" measures from the chief ray (for the
wavefront: the OPD as it is, relative to the chief ray) and "centroid" from the centroid of the spot (for the
wavefront: the mean OPD removed). The pupil is hexapolar with *rings* rings out to *pupil_fill*. The row weight is sqrt(field_weight x wavelength_weight) (default 1). The EFL hold is an EFFL row with target *efl_target* (default the starting EFL; False for none) and weight efl_weight / |target|; the default *efl_weight* is 10 sqrt(n_rays) for the spot and 100 sqrt(n_rays) for the wavefront. For kind="spot", reference="chief" the total equals lensopt.merit_function on the same pupil.
Returns {"operands", "fields", "wavelengths", "kind", "reference", "rings", "pupil_fill", "efl_target", "efl_weight", "n_rays"}; pass it (or its "operands" with fields= and wavelengths=) to the other ops.
Example::
m = merit_default(lens, kind="wavefront", rings=3)
merit_evaluate(lens, m)["merit"]
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.
• optimized_lens_tolerance_sensitivity — py -3.11 examples/optimized_lens_tolerance_sensitivity.py
• poc_lens_merit_operands — py -3.11 examples/poc_lens_merit_operands.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
lensmerit)merit_operands_list · merit_evaluate · merit_boundary_operands · optimize_lens_operands · merit_contributions
*Provenance: lensmerit.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.