Source code for dolfinx.geometry

# Copyright (C) 2018-2021 Michal Habera, Garth N. Wells and Jørgen S. Dokken
# This file is part of DOLFINx (
# SPDX-License-Identifier:    LGPL-3.0-or-later
"""Methods for geometric searches and operations"""

from __future__ import annotations

import typing

import numpy as np
import numpy.typing as npt

if typing.TYPE_CHECKING:
    from dolfinx.mesh import Mesh
    from dolfinx.cpp.graph import AdjacencyList_int32

import numpy

from dolfinx import cpp as _cpp
from dolfinx.cpp.geometry import compute_collisions, compute_distance_gjk

__all__ = ["compute_colliding_cells", "squared_distance", "compute_closest_entity", "compute_collisions",
           "compute_distance_gjk", "create_midpoint_tree"]

class BoundingBoxTree(_cpp.geometry.BoundingBoxTree):
    """Bounding box trees used in collision detection."""

    def __init__(self, mesh: Mesh, dim: int, entities=None, padding: float = 0.0):
        """Create a bounding box tree for entities of a mesh.

            mesh: The mesh
            dim: The dimension of the mesh entities
            entities: List of entity indices (local to process). If not supplied,
                all owned and ghosted entities are used.
            padding: Padding for each bounding box

        map = mesh.topology.index_map(dim)
        if map is None:
            raise RuntimeError(f"Mesh entities of dimension {dim} have not been created.")
        if entities is None:
            entities = range(0, map.size_local + map.num_ghosts)

        super().__init__(mesh._cpp_object, dim, entities, padding)

[docs]def compute_closest_entity(tree: BoundingBoxTree, midpoint_tree: BoundingBoxTree, mesh: Mesh, points: numpy.ndarray) -> npt.NDArray[np.int32]: """Compute closest mesh entity to a point. Args: tree: bounding box tree for the entities midpoint_tree: A bounding box tree with the midpoints of all the mesh entities. This is used to accelerate the search. mesh: The mesh points: The points to check for collision, shape=(num_points, 3) Returns: Mesh entity index for each point in `points`. Returns -1 for a point if the bounding box tree is empty. """ return _cpp.geometry.compute_closest_entity(tree, midpoint_tree, mesh._cpp_object, points)
[docs]def create_midpoint_tree(mesh: Mesh, dim: int, entities: numpy.ndarray): return _cpp.geometry.create_midpoint_tree(mesh._cpp_object, dim, entities)
[docs]def compute_colliding_cells(mesh: Mesh, candidates: AdjacencyList_int32, x: numpy.ndarray): """From a mesh, find which cells collide with a set of points. Args: mesh: The mesh candidate_cells: Adjacency list of candidate colliding cells for the ith point in `x` points: The points to check for collision shape=(num_points, 3) Returns: Adjacency list where the ith node is the list of entities that collide with the ith point """ return _cpp.geometry.compute_colliding_cells(mesh._cpp_object, candidates, x)
[docs]def squared_distance(mesh: Mesh, dim: int, entities: typing.List[int], points: numpy.ndarray): """Compute the squared distance between a point and a mesh entity. The distance is computed between the ith input points and the ith input entity. Args: mesh: Mesh containing the entities dim: The topological dimension of the mesh entities entities: Indices of the mesh entities (local to process) points: Set points from which to computed the shortest distance (``shape=(num_points, 3)``) Returns: Squared shortest distance from points[i] to entities[i] """ return _cpp.geometry.squared_distance(mesh._cpp_object, dim, entities, points)