tb_cx_log_magnitude — 2D `typed` op

• Data kinds: cimage → image

• Call: fullseye.apply(img, "tb_cx_log_magnitude", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

*No figure: this op takes cimage as input. A Studio program starting from an image cannot reach that type — see the runnable examples below for how it is used.*

Usage

Log magnitude spectrum for display -> real `(H, W) float64 in [0, 1]`.

`log1p(|cx|) (i.e. log(1 + |cx|)`, finite at zero) min-max normalised to

`[0, 1]`. This is the conventional way to see a spectrum whose linear

magnitude is dominated by the DC spike. Display only — it is not invertible;

use :func:cx_magnitude for the metric magnitude.

Typed bridge of the 2d op `cx_log_magnitude into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. This op has no tunable parameter; a and b` are unused.

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.

Runnable examples (verified samples that actually call this op)

• (none yet)

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

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`typed`)

tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

tb_cx_log_magnitude — 2D typed op