lensglobal op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.lens_global_search(system=None, variables=None, merit=None, n_starts: 'int' = 8, seed: 'int' = 0, spread: 'float' = 0.5, iterations: 'int' = 40, duplicate_tolerance: 'float' = 0.001) -> 'dict' (to call the implementation directly, import lensglobal; lensglobal.lens_global_search(system=None, variables=None, merit=None, n_starts: 'int' = 8, seed: 'int' = 0, spread: 'float' = 0.5, iterations: 'int' = 40, duplicate_tolerance: 'float' = 0.001) -> 'dict'; from the registry, opsoptics.get("lens_global_search"))
Collect local optima with multi-start local optimisation (lensopt.optimize_lens) and return them in merit order (table).
Start 0 is *system* as given; starts 1 .. *n_starts*-1 move every variable to x (1 + spread u) with u uniform in [-1, 1] drawn from numpy.random.default_rng(seed) (a variable that is zero, such as a flat, moves by *spread* times the typical size of its kind). A perturbed start that is not a valid prescription is redrawn (up to 25 times, then ValueError). Every start is optimised by lensopt.optimize_lens with the *same* merit and the *same* EFL target (the EFL of *system* unless merit["efl_target"] says otherwise), so the optima are comparable. *merit* is a dict of fields wavelengths rings efl_target efl_weight field_weights pupil_fill min_thickness max_thickness min_radius damping tolerance; *variables* are as in optimize_lens.
Returns {"optima", "best", "n_starts", "n_distinct", "basins", "seed", "spread"}. optima holds every start's result sorted by merit (ties by start index): {"start", "merit", "start_merit", "rms_spot", "efl", "system", "values", "status", "converged", "duplicate_of"} where duplicate_of is the start of an earlier (better) optimum with the same variables (every variable within *duplicate_tolerance* relative, floor 1e-9) or None. basins lists the distinct optima with the number of starts that reached each. Deterministic for a given seed.
Example::
r = lens_global_search(singlet, ["c0", "c1"], {"efl_target": 100.0}, n_starts=12, seed=3)
r["best"]["merit"], r["n_distinct"]
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_lens_global_search — py -3.11 examples/poc_lens_global_search.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
lensglobal)glass_nearest · lens_substitute_glasses
*Provenance: lensglobal.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.