tb_smooth_funct_1d_gauss — 2D `typed` op

• Data kinds: signal → signal

• Call: fullseye.apply(img, "tb_smooth_funct_1d_gauss", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Gaussian smoothing of a 1-D function (HALCON `smooth_funct_1d_gauss`).

Convolves *y* with a Gaussian of standard deviation *sigma* (in samples),

`reflect` boundary handling (scipy default). The DC level is preserved;

zero-mean noise variance shrinks by roughly `1 / (2 * sigma * sqrt(pi))`.

:param y: 1-D function, at least 1 sample.

:param sigma: Gaussian standard deviation in samples; must be finite and > 0.

:returns: smoothed float64 array, same length as *y*.

:raises ValueError: non-1-D / NaN / Inf input, empty input, or `sigma <= 0`.

Typed bridge of the 1d op `smooth_funct_1d_gauss into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives sigma (default 1); b` is 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 `signal` as input)

identity · tb_create_funct_1d_array · tb_smooth_funct_1d_mean · tb_derivate_funct_1d · tb_integrate_funct_1d · tb_zero_crossings_funct_1d · tb_abs_funct_1d · tb_negate_funct_1d

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_smooth_funct_1d_gauss — 2D typed op