layout op• 데이터 종류: 없음 → signal(인자만으로 정해지는 연산자 —— 이미지나 데이터 입력을 받지 않습니다)
• 호출: import tomography; tomography.projection_angles(n_angles=180, span_deg=180.0, scheme='uniform', start_deg=0.0)(또는 opstomography.get("projection_angles"))
스캔의 각도 열을 도 단위 1-D float64 배열로.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Three schemes, and the difference between them is what happens when a scan is
cut short:
• `"uniform" — start + span * k / n`. The textbook scan. A prefix of it
covers only a wedge, so an interrupted uniform scan is a limited-angle scan.
• `"golden" — increments of 180/phi = 111.246...` degrees, wrapped into
`[start, start+span)`. Every prefix is near-uniform, so an interrupted
golden scan is a *sparse* scan, which is a far easier problem. Largest
angular gap left by a scan that stops early, measured:
scheme after 32 of 180 all 180
uniform 149.000 deg 1.000 deg
golden 10.031 deg 1.464 deg
bit-reversed 8.000 deg 1.000 deg
Uniform's 149-degree hole is the entire limited-angle problem arriving by
accident. Bit-reversed is the only one of the three that is good at both
ends; golden's price for working at *every* prefix length rather than only
at powers of two is a completed set 1.46x less even than the grid.
• `"bit-reversed"` — the uniform grid, visited in bit-reversed order. Same
guarantee as golden for power-of-two prefixes and exactly uniform at the
end, which golden is not.
*span_deg* is the total angular range. 180 degrees is the complete data set
for parallel beam — projections at `theta and theta+180` are mirror
images and carry no new information — so a 360-degree span is redundancy, not
resolution, and anything under 180 is the limited-angle problem.
:param n_angles: number of views, `1 .. 65536`.
:param span_deg: total angular range in degrees, `> 0`.
:param scheme: one of :data:ANGLE_SCHEMES.
:param start_deg: angle of the first view.
:returns: `(n_angles,)` float64 array of degrees.
:raises ValueError: on a non-int count, a non-positive span, a non-finite
start, or an unknown scheme.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• ct_reconstruction — py -3.11 examples/ct_reconstruction.py
• tomography_reconstruct — py -3.11 examples/tomography_reconstruct.py
signal 를 입력으로 받는 것)—
layout)*Provenance: tomography.py — TOMOGRAPHY 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.