points
PointsSampler
__init__()
group_points(sample_coords, all_coords, point_ids=None)
staticmethod
Select regular points and group other points using Voronoi tessellation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
sample_coords
|
ndarray[N, 3]
|
Coords of point selection. |
required |
all_coords
|
ndarray[N, 3]
|
Coords of all points. |
required |
point_ids
|
ndarray
|
Points ids of sample_coords. |
None
|
Returns:
| Name | Type | Description |
|---|---|---|
voronoi_ids |
tuple
|
Indices of the selected points and group labels (selected point ids) for each input points. |
select_random_points(point_ids, sample_size, seed=568)
staticmethod
Select random points from point cloud.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
point_ids
|
ndarray[N,]
|
Indices of the points. |
required |
sample_size
|
int
|
Number of points to select. |
required |
seed
|
int
|
Seed for random number generator. |
568
|
Returns:
| Type | Description |
|---|---|
ndarray
|
np.ndarray[M,]: Indices of the selected points. |
select_regular_points(coords, distance)
staticmethod
Select regular points from point cloud by removing points within a given distance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
coords
|
ndarray[N, 3]
|
Coordinates of points. |
required |
distance
|
float
|
Minimal distance between points. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
np.ndarray[M,]: Indices of the selected points. |
PointsStats
Class for computing statistics within the distance.
Attributes:
| Name | Type | Description |
|---|---|---|
coords |
Coordinates of the points |
|
neighbors |
Pair of neighbor indexes |
coords = coords
instance-attribute
neighbors = None
instance-attribute
__init__(coords)
Initialize.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
coords
|
ndarray[N, 3]
|
Coordinates of the points |
required |
make_neighbors(radius=10)
Find pairs of points which form neighborhoods.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
radius
|
int
|
All points within |
10
|
Returns:
| Type | Description |
|---|---|
ndarray
|
np.ndarray[M, 2]: Pair of neighbors |
max(scalars)
Calculate the maximum of scalars within the neighborhood.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
scalars
|
ndarray[N,]
|
Array of scalar values. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
np.ndarray[N,]: Maximum values in neighborhoods. |
mean(scalars)
Calculate the mean of scalars within the neighborhood.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
scalars
|
ndarray[N,]
|
Array of scalar values. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
np.ndarray[N,]: Mean values in neighborhoods. |
std(scalars)
Calculate the standard deviation of scalars within the neighborhood.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
scalars
|
ndarray[N,]
|
Array of scalar values. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
np.ndarray[N,]: Standard deviation values in neighborhoods. |
ProjectPointToSurface
__init__()
nearest_distance(input_coords, source_coords, max_distance=np.inf)
staticmethod
Find nearest points.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
input_coords
|
ndarray[M, 3]
|
Surface points. |
required |
source_coords
|
ndarray[N, 3]
|
Data points. |
required |
max_distance
|
float
|
The maximum distance within which to search for nearest mesh points. Defaults to np.inf. |
inf
|
Returns:
| Name | Type | Description |
|---|---|---|
tuple |
tuple[float, list]
|
Closest distance and index of the points. |
nearest_points(input_coords, source_coords, max_distance=np.inf)
staticmethod
Projects source_coords to input_coords by finding the nearest points within a maximum distance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
input_coords
|
ndarray[M, 3]
|
Surface points. |
required |
source_coords
|
ndarray[N, 3]
|
Data points. |
required |
max_distance
|
float
|
The maximum distance within which to search for nearest mesh points. Defaults to np.inf. |
inf
|
Returns:
| Name | Type | Description |
|---|---|---|
tuple |
tuple
|
Point indices and index labels for each source_coords. |