r_value_map — TOMOGRAPHY `spectral_xray` op

• Data kinds: images → image2d

• Call: import fullseye as fs; fs.ledger.r_value_map(signal, channels=None, photons_per_pixel: 'float' = 100000.0, low: 'int' = 0, high: 'int' = -1, flat=None, min_log: 'float' = 0.02) -> 'np.ndarray' (to call the implementation directly, import dualxray; dualxray.r_value_map(signal, channels=None, photons_per_pixel: 'float' = 100000.0, low: 'int' = 0, high: 'int' = -1, flat=None, min_log: 'float' = 0.02) -> 'np.ndarray'; from the registry, opstomography.get("r_value_map"))

Usage

Return the image of R = L_low / L_high (ratio of low- to high-energy log attenuation).

> The detailed description below is the original text — the summary and the headings are translated.

単色なら R = μ(E_low)/μ(E_high) で厚みに依らず材質だけで決まる(Z が大きいほど光電効果で低エネルギーの μ が大きく、R が大きい)。多色ではビーム硬化で厚みとともに少し動く —— その動きが「厚い軽元素と薄い重元素」の交絡の源。L_high < min_log の画素(ほぼ素通しで比が雑音だけになる)と信号が 0 以下の画素は NaN(拒否)。

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• (none yet)

Ops the type connects to (they accept `image2d` as input)

radon_transform

Same category (`spectral_xray`)

material_xray_table · material_xray_attenuation · material_xray_constants · material_effective_z · xray_tube_spectrum · xray_beam_quality · photon_counting_channels · dual_kvp_channels

*Provenance: dualxray.py — TOMOGRAPHY operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

r_value_map — TOMOGRAPHY spectral_xray op