siibra.locations.pointcloud

A set of coordinates on a reference space.

Classes

Contour

A PointCloud that represents a contour line.

PointCloud

A set of 3D points in the same reference space,

Functions

from_points(→ PointCloud)

Create a PointCloud from an iterable of Points.

Module Contents

class siibra.locations.pointcloud.Contour(coordinates, space=None, sigma_mm=0, labels: list = None)
Inheritance diagram of siibra.locations.pointcloud.Contour

A PointCloud that represents a contour line. The only difference is that the point order is relevant, and consecutive points are thought as being connected by an edge.

In fact, PointCloud assumes order as well, but no connections between points.

crop(voi: siibra.locations.boundingbox.BoundingBox) → List[Contour]

Crop the contour with a volume of interest. Since the contour might be split from the cropping, returns a set of contour segments.

class siibra.locations.pointcloud.PointCloud(coordinates: List[Tuple] | numpy.ndarray, space=None, sigma_mm: int | float | List[int | float] = 0, labels: List[int | float | tuple] = None)
Inheritance diagram of siibra.locations.pointcloud.PointCloud

A set of 3D points in the same reference space, defined by a list of coordinates.

__eq__(other: PointCloud)

Required to provide comparison and making the object hashable

__getitem__(index: int)
__hash__()
__iter__()

Return an iterator over the coordinate locations.

__len__()

The number of points in this PointCloud.

__str__()
as_list()

Return the point set as a list of 3D tuples.

find_clusters(min_fraction: float = 1 / 200, max_fraction: float = 1 / 8) → List[int]

Find clusters using HDBSCAN (https://dl.acm.org/doi/10.1145/2733381) implementation of scikit-learn (https://dl.acm.org/doi/10.5555/1953048.2078195).

Parameters:

  • min_fraction (min cluster size as a fraction of total points in the PointCloud) –

  • max_fraction (max cluster size as a fraction of total points in the PointCloud) –

Returns:

Returns the cluster labels found by skilearn.cluster.HDBSCAN.

Note

Replaces the labels of the PointCloud instance with these labels.

Return type:

List[int]

Raises:

RuntimeError – If a sklearn version without HDBSCAN is installed.

intersection(other: siibra.locations.location.Location)

Return the subset of points that are inside the given mask.

NOTE: The affine matrix of the image must be set to warp voxels coordinates into the reference space of this Bounding Box.

transform(affine: numpy.ndarray, space=None)

Returns a new PointCloud obtained by transforming the coordinates of this one with the given affine matrix.

Parameters:

  • affine (numpy 4x4 ndarray) – affine matrix

  • space (reference space (id, name, or Space)) – Target reference space which is reached after applying the transform. Note that the consistency of this cannot be checked and is up to the user.

warp(space: str | Dict | siibra.core.space.Space, chunksize: int = 1000)

Creates a new point set by warping its points to another space

property boundingbox

Return the bounding box of these points, or None in the special case of an empty PointCloud.

property centroid
property coordinates: numpy.ndarray
property has_constant_sigma: bool
property homogeneous

Access the list of 3D point as an Nx4 array of homogeneous coordinates.

property label_colors

return a color for the given label.

labels = None
property sigma: List[int | float]

The list of sigmas corresponding to the points.

property volume
siibra.locations.pointcloud.from_points(points: List[siibra.locations.point.Point], newlabels: List[int | float | tuple] = None) → PointCloud

Create a PointCloud from an iterable of Points.

Parameters:

  • points (Iterable[point.Point]) –

  • newlabels (List[int], optional) – Use these labels instead of the original labels of the points.

Return type:

PointCloud