montecarlo op• Data kinds: कोई नहीं → table (केवल arguments से तय होने वाला op — यह कोई image या data input नहीं लेता)
• Call: import fullseye as fs; fs.ledger.mc_photon_slab(layers=None, n_above=1.0, n_below=1.0, n_photons=100000, tol=None, confidence=0.95, batch=50000, seed=1, w_threshold=0.0001, roulette_m=10) -> 'dict' (implementation को सीधे call करना हो तो import mcphoton; mcphoton.mc_photon_slab(layers=None, n_above=1.0, n_below=1.0, n_photons=100000, tol=None, confidence=0.95, batch=50000, seed=1, w_threshold=0.0001, roulette_m=10) -> 'dict', registry से लेना हो तो opsconverge.get("mc_photon_slab"))
MCML-type Monte Carlo से layered turbid slab का diffuse reflectance, total transmittance और absorption, confidence intervals के साथ।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
layers: list of {"n", "mua", "mus", "g", "d"} from the top (coefficients in 1/length, thickness in the same length; d = inf for a semi-infinite last layer). n_above / n_below: ambient refractive indices. A pencil beam enters at normal incidence.
The tracing rules are those of the MCML manual (Wang & Jacques), eqs. 3.2a-3.31 — see the module docstring for which equation does what. Every packet's contribution is kept, so the standard error is the sample standard deviation over packets / sqrt(N) and the interval is mean +- z se (confidence). Batch b uses default_rng((seed, b)).
• tol=None: trace n_photons packets.
• tol=h: sequential Monte Carlo — add batches of batch packets until the half-width of R_d and of T_t is <= h (the stopping rule of converge, from the second batch on), with n_photons as the budget: refusing (NotConvergedError) if the budget is spent first.
Returns Rd, Tt, A (absorbed fraction) and A_layers, each with *_se and *_ci; Rsp (specular, deterministic: eq. 3.2a); energy_balance = R_sp + R_d + T_t + A (1 in expectation — roulette conserves energy only on average, so its deviation is compared with its own standard error); R_total = R_sp + R_d (specular plus diffuse, with the interval of R_d shifted by the deterministic R_sp); n_photons. Defaults: the MCML manual Table 6.1 slab (van de Hulst: R_d = 0.09739, T_t = 0.66096); MCML_TABLE_6_2 holds Table 6.2.
• convergent_computation 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।
• converge_tour — py -3.11 examples/converge_tour.py
table को input के रूप में लेते हैं)—
montecarlo)*Provenance: mcphoton.py — CONVERGE 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.