mc_photon_slab — CONVERGE `montecarlo` op

• データ種: なし → table(引数だけで決まる op —— 画像やデータの入力を取らない)

• 呼び出し: 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' (実装を直接呼ぶなら 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'、台帳から引くなら opsconverge.get("mc_photon_slab"))

使い方

Diffuse reflectance, total transmittance and absorption of a layered turbid slab by MCML-type Monte Carlo, with confidence intervals.

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.

詳しい使い方ガイド

• convergent_computation ファミリガイド

参考(サンプルデータ・文献)

• サンプルデータカタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。

• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。

• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• converge_tour — py -3.11 examples/converge_tour.py

型が繋がる次の op(`table` を入力に取れる)

—

同カテゴリ(`montecarlo`)

mc_sequential_mean

*Provenance: mcphoton.py — CONVERGE operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*

mc_photon_slab — CONVERGE montecarlo op

使い方