sensorchar op• Data kinds: matrix → table
• Call: import fullseye as fs; fs.ledger.emva_filter_distortion(kernel, temporal_alpha=1.0) (to call the implementation directly, import sensorchar; sensorchar.emva_filter_distortion(kernel, temporal_alpha=1.0); from the registry, opsoptics.get("emva_filter_distortion"))
How much in-camera linear preprocessing moves the EMVA 1288 Linear numbers: closed forms (this family's derivation, not equations of the standard).
> The detailed description below is the original text — the summary and the headings are translated.
*kernel*: 画像ごとにかかる 2-D の線形フィルタ w(1 × 1 なら数字のゲイン g)。*temporal_alpha*: 続けた画像にかかる 1 次の再帰 y_t = α x_t + (1 − α) y_{t−1}(1 = 時間フィルタ無し)。平均は G = Σw 倍、白い時間雑音の分散は Σw² 倍になる。2 枚の差の分散(式 18)は再帰で α²/(2 − α) 倍になる。S = (Σw²/G²) · α²/(2 − α) とおくと、photon transfer(式 50)の傾き K' = G S K、応答 R' = G R(式 49)、η' = R'/K' = η/S(式 52)、σ_d' = σ_d/√S(式 53、量子化を無視)、SNR(μ_p)' = SNR/√S(式 21)。固定パターンは空間の w だけを通る: 白い画素の模様の分散は Σw² 倍、列の模様は Σ_n(Σ_m w[m][n])² 倍、行も同様。だから DSNU1288' [e⁻] = DSNU · √(gain_x)/S、PRNU1288' = PRNU · √(gain_x)(gain_x = 正規化した各成分の利得)。隣の画素の時間雑音の相関の予測 ρ_h・ρ_v・ρ_d と分散比(emva_noise_whiteness と同じ定義)も返す。
前提(破ると閉形式は近似): 量子化と飽和を無視、空間の境界は周期(端の画素を考えない)、雑音と模様は白い。
返り値 {"G", "sum_w2", "S_spatial", "S_temporal", "S", "K_factor", "R_factor", "eta_factor", "sigma_d_factor", "snr_factor", "dsnu_e_factor": {"pixel", "col", "row"}, "prnu_factor": {...}, "pattern_gain": {...}, "rho_h", "rho_v", "rho_d", "variance_ratio_2x2", "rho_t"}。
Raises ValueError: 2-D の実数でない / NaN・inf / Σw が正でない(平均の符号が反転・消える)/ temporal_alpha が (0, 1] の外。
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_emva1288_in_camera_processing — py -3.11 examples/poc_emva1288_in_camera_processing.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
sensorchar)emva_pair_statistics · emva_photon_transfer · emva_quantum_efficiency · emva_linearity_error · emva_snr_curve · emva_sensitivity_threshold · emva_dynamic_range · emva_dark_current
*Provenance: sensorchar.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.