Cloud#

Cloud of points classes.

class volmdlr.cloud.PointCloud2D(points, name: str = '')#

Bases: DessiaObject

Point Cloud2D class.

Parameters:

points – list of points for point cloud.

bounding_rectangle()#

Calculate the bounding rectangle for the point cloud.

Returns:

bounds for bounding rectangle.

plot(ax=None, color='k')#

Plot a point cloud 2d using Matplotlib.

simplify(resolution=5)#

Simplify cloud point 2d.

to_coord_matrix() → list[list[float]]#

Generate an n_points x 2 matrix of coordinates.

to_polygon(convex=False)#

Use a Cloud point 2d to create a polygon.

Parameters:

convex – if True, it will return a convex polygon. If false, it will search for a concave polygon.

Returns:

closed polygon 2d.

class volmdlr.cloud.PointCloud3D(points: list[Point3D], name: str = '')#

Bases: DessiaObject

Point Cloud3D, a list of points.

Parameters:

points – a list of points.

property bounding_box#

Property to get the bounding box of the point cloud.

Returns:

Bounding box object.

static check_area_polygon(initial_polygons2d, position_plane, normal, vec1, vec2)#

Check and processes area of polygons.

Parameters:

  • initial_polygons2d – List of 2D polygons.

  • position_plane – Position of planes.

  • normal – Normal vector.

  • vec1 – Vector 1.

  • vec2 – Vector 2.

Returns:

List of processed 3D polygons.

determine_extrusion_vector()#

Determine the extrusion vector based on the bounding box.

Returns:

Tuple containing position, normal, vec1, and vec2.

extended_cloud(distance_extended: float)#

Extend cloud of point by a given distance.

Parameters:

distance_extended – distance to extend cloud by.

Returns:

extended points.

extract(u, umin, umax)#

Extract a subset of points within a given range from a plane.

Parameters:

  • u – Normal vector of the plane.

  • umin – Minimum distance to the plane.

  • umax – Maximum distance to the plane.

Returns:

PointCloud3D object containing the extracted points.

classmethod from_step(step_file: str, name: str = '')#

Create a cloud of points from a step file.

Parameters:

  • step_file – step file.

  • name – object’s name.

Returns:

Point Cloud 3D.

classmethod from_stl(file_path, name: str = 'from_stl')#

Create a point cloud 3d from a stl file.

Parameters:

  • file_path – path to stl file.

  • name – object’s name.

Returns:

point cloud 3d object.

classmethod generate_shell(polygons_3d: list[ClosedPolygon3D], normal: Vector3D, vec1: Vector3D, vec2: Vector3D, name: str = '')#

Generate a shell from a list of polygon 3d, using a sewing algorithm.

Parameters:

  • polygons_3d – list of polygon 3d to be sewed.

  • normal – normal to the sewing plane.

  • vec1 – u vector in the sewing plane.

  • vec2 – v vector in the sewing plane.

  • name – object’s name.

Returns:

return a shell.

static offset_to_shell(positions_plane: list[Plane3D], polygons2d: list[ClosedPolygon2D], offset: float)#

Add offset to a shell.

plot(ax=None, color='k')#

Plot the cloud 3d.

position_plane(posmax, resolution)#

Calculate the position of planes.

Parameters:

  • posmax – Position index.

  • resolution – Resolution of the plane.

Returns:

Tuple containing distance between planes and position of planes.

shell_distances(shells: OpenTriangleShell3D) → tuple[PointCloud3D, list[float], list[int]]#

Compute distance of point to shell for each point in self.points.

Returns:

The point cloud of points projection on nearest triangle, their distances and the corresponding

triangles index :rtype: tuple[PointCloud3D, list[float], list[int]]

shell_distances_ndarray(shells: OpenTriangleShell3D)#

Compute distance of point to shell for each point in self.points in a numpy formatted data.

Returns:

The point cloud of points projection on nearest triangle, their distances and the corresponding

triangles index :rtype: tuple[numpy.ndarray(float), numpy.ndarray(float), numpy.ndarray(int)]

to_2d(plane_origin, x, y)#

Projects the point cloud on a 2D plane.

Parameters:

  • plane_origin – Origin of the plane in 3D.

  • x – X-axis vector in 3D.

  • y – Y-axis vector in 3D.

Returns:

PointCloud2D object.

to_coord_matrix() → list[list[float]]#

Generate an n_points x 3 matrix of coordinates.

to_shell(resolution: int = 10, normal=None, offset: float = 0)#

Create a Shell from a Cloud of points 3D.

to_subcloud2d(pos_normal, vec1, vec2)#

Convert the point cloud to a simplified 2D sub-cloud.

Parameters:

  • pos_normal – Position and normal vector.

  • vec1 – Vector 1.

  • vec2 – Vector 2.

Returns:

Simplified PointCloud2D object.