simulate op• Data kinds: कोई नहीं → sweep (केवल arguments से तय होने वाला op — यह कोई image या data input नहीं लेता)
• Call: import fullseye as fs; fs.ledger.chromatic_confocal_simulate(surface_um=0.0, wavelength_start_nm=500.0, wavelength_step_nm=0.5, n_bins=401, dispersion_um_per_nm=0.2, reference_wavelength_nm=600.0, peak_fwhm_nm=4.0, peak_counts=1000.0, background=10.0, noise=0.0, seed=0) (implementation को सीधे call करना हो तो import interferometry; interferometry.chromatic_confocal_simulate(surface_um=0.0, wavelength_start_nm=500.0, wavelength_step_nm=0.5, n_bins=401, dispersion_um_per_nm=0.2, reference_wavelength_nm=600.0, peak_fwhm_nm=4.0, peak_counts=1000.0, background=10.0, noise=0.0, seed=0), registry से लेना हो तो opsinterferometry.get("chromatic_confocal_simulate"))
ज्ञात height पर surface का confocal return spectrum synthesise करता है।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
A chromatic objective is built to have axial colour on purpose: each wavelength focuses at a different height, so only the wavelength focused *on the surface* passes the confocal pinhole. The spectrometer therefore sees a peak whose wavelength is the height::
lambda_peak = reference_wavelength_nm
+ (surface_um - 0) / dispersion_um_per_nm
i.e. surface_um = (lambda_peak - reference_wavelength_nm) * dispersion_um_per_nm, which is what chromatic_confocal_height inverts. The peak is modelled as a Gaussian of FWHM *peak_fwhm_nm* on a flat *background* pedestal.
surface_um: true height (0 = the reference wavelength focuses
exactly on it). May be negative — unlike a
time-of-flight distance, a height is signed.
wavelength_start_nm / wavelength_step_nm / n_bins: the spectrometer axis.
dispersion_um_per_nm: the axial chromatic dispersion, height per
nanometre. This is the calibration constant and
the units are in the name for a reason: a
per-micrometre reading of it is a 1000x error in
the height.
peak_fwhm_nm: spectral width of the confocal response.
peak_counts / background: peak height above, and level of, the pedestal.
noise / seed: additive Gaussian sigma and its integer seed.
Returns a 1-D float64 spectrum of n_bins non-negative intensities (clipped at 0, because a spectrometer cannot read negative light — and the clip is stated here rather than left as a surprise).
Ground truth: with noise=0 the "gaussian" estimator recovers *surface_um* exactly (measured 0.0e+00 to 3.6e-15 um over heights from -15 to +18 um), at any peak width and even with the peak two bins from the band edge, because the logarithm of a sampled Gaussian is exactly a parabola and the three-point fit is *local* — there is no Hilbert transform here, so the truncation failure that limits the coherence-scanning side does not exist on this one (pinned in the tests).
Raises ValueError: non-real / non-finite / string / bool parameters, a non-positive step / width / dispersion, negative *peak_counts* / *background* / *noise*, *n_bins* outside [3, MAX_SCAN_POINTS], and a *surface_um* whose wavelength falls outside the spectrometer band (the out-of-range case a real probe reports as "no surface").
• coherence_scanning family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• coherence_scanning — py -3.11 examples/coherence_scanning.py
sweep को input के रूप में लेते हैं)csi_envelope · csi_peak_position · chromatic_confocal_height
simulate)csi_signal_simulate · csi_stack_simulate
*Provenance: interferometry.py — INTERFEROMETRY operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.