brain_deer.plugins.warpdrive.geometry¶

Geometry helpers for WarpDrive tools (plane rings, contours, path resample).

Pure numpy (+ optional nibabel); no Qt. Used by Draw / ShrinkExpand tools and tests.

Functions¶

`plane_basis`(→ tuple[FloatArray, FloatArray])	Return `(plane_normal, in_plane_u)` unit vectors for a slice view.
`resample_path`(→ list[WorldPoint])	Polyline resampled at approximately `step_mm` spacing (keeps endpoints).
`shrink_expand_ring`(→ tuple[list[WorldPoint], ...)	Build source/target rings in the slice plane (Slicer ShrinkExpand parity).
`label_slice_contour`(→ FloatArray)	Boundary voxels of a label mask on one slice, as RAS mm points `(N,3)`.
`nearest_points_on_cloud`(→ FloatArray)	For each query, the closest point in `cloud` (brute force; contours are small).
`match_path_counts`(→ tuple[list[WorldPoint], ...)	Resample the longer path so both have the same point count (min length ≥ 2).
`best_cloud_for_seed`(→ FloatArray)	Pick the contour cloud whose nearest point is closest to `seed`.

Module Contents¶

brain_deer.plugins.warpdrive.geometry.plane_basis(view_name: str) → tuple[FloatArray, FloatArray]¶: Return (plane_normal, in_plane_u) unit vectors for a slice view.

brain_deer.plugins.warpdrive.geometry.resample_path(path: Sequence[WorldPoint], step_mm: float) → list[WorldPoint]¶: Polyline resampled at approximately step_mm spacing (keeps endpoints).

brain_deer.plugins.warpdrive.geometry.shrink_expand_ring(center: WorldPoint, view_name: str, radius_mm: float, amount_pct: float, *, mode: str = 'Shrink', n_angles: int = 12) → tuple[list[WorldPoint], list[WorldPoint]]¶

Build source/target rings in the slice plane (Slicer ShrinkExpand parity).

amount_pct is 5–95. Shrink pulls the outer ring inward; Expand pushes out. Returns (source_points, target_points) each of length n_angles.

brain_deer.plugins.warpdrive.geometry.label_slice_contour(data: FloatArray, affine: FloatArray, slice_axis: int, slice_index: int, *, label: int | None = None) → FloatArray¶: Boundary voxels of a label mask on one slice, as RAS mm points (N,3).

brain_deer.plugins.warpdrive.geometry.nearest_points_on_cloud(queries: FloatArray, cloud: FloatArray) → FloatArray¶: For each query, the closest point in cloud (brute force; contours are small).

brain_deer.plugins.warpdrive.geometry.match_path_counts(source: Sequence[WorldPoint], target: Sequence[WorldPoint]) → tuple[list[WorldPoint], list[WorldPoint]]¶: Resample the longer path so both have the same point count (min length ≥ 2).

brain_deer.plugins.warpdrive.geometry.best_cloud_for_seed(seed: WorldPoint, clouds: Iterable[FloatArray]) → FloatArray¶: Pick the contour cloud whose nearest point is closest to seed.