sec_interp.core.utils.geometry_utils.optimization module

class sec_interp.core.utils.geometry_utils.optimization.PreviewOptimizer

Bases: object

Provides geometric optimization and sampling logic for preview rendering.

classmethod adaptive_sample(data: list[tuple[float, float]], min_tolerance: float = 0.1, max_tolerance: float = 10.0, max_points: int = 1000) → list[tuple[float, float]]

Adaptively sample data based on local curvature.

Parameters:

• data – List of (x, y) tuples

• min_tolerance – Minimum tolerance for high-detail areas

• max_tolerance – Maximum tolerance for low-detail areas

• max_points – Maximum points to keep (approximate target)

Returns:

Adaptively sampled data

static calculate_curvature(data: list[tuple[float, float]]) → list[float]

Calculate a simple curvature metric for each point in a line.

This approximates curvature by the angle deviation between successive segments. High values indicate sharper turns.

Parameters:

data – List of (x, y) tuples.

Returns:

List of curvature values (angles in degrees), same length as data.

static decimate(data: list[tuple[float, float]], tolerance: float | None = None, max_points: int = 1000) → list[tuple[float, float]]

Decimate line data using Douglas-Peucker algorithm.

Parameters:

• data – List of (x, y) tuples

• tolerance – Simplification tolerance (if provided, overrides max_points heuristic)

• max_points – Maximum points to keep (approximate target if tolerance is None)

Returns:

Decimated list of (x, y) tuples