CCD¶

Broad Phase¶

class ipctk.BroadPhaseMethod¶

Enumeration of implemented broad phase methods.

Members:

BRUTE_FORCE :

HASH_GRID :

SPATIAL_HASH :

SWEEP_AND_TINIEST_QUEUE :

Candidates¶

class ipctk.ContinuousCollisionCandidate¶

ccd(self, V0: numpy.ndarray[numpy.float64[m, n]], V1: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], min_distance: float = 0.0, tmax: float = 1.0, tolerance: float = 1e-06, max_iterations: int = 10000000, conservative_rescaling: float = 0.8) → tuple[bool, float]¶

Perform narrow-phase CCD on the candidate.

Parameters:¶

V0: numpy.ndarray[numpy.float64[m, n]]¶: Mesh vertex positions at the start of the time step.
V1: numpy.ndarray[numpy.float64[m, n]]¶: Mesh vertex positions at the end of the time step.
E: numpy.ndarray[numpy.int32[m, n]]¶: Mesh edges as rows of indicies into V.
F: numpy.ndarray[numpy.int32[m, n]]¶: Mesh triangular faces as rows of indicies into V.
tmax: float = 1.0¶: Maximum time (normalized) to look for collisions. Should be in [0, 1].
tolerance: float = 1e-06¶: CCD tolerance used by Tight-Inclusion CCD.
max_iterations: int = 10000000¶: Maximum iterations used by Tight-Inclusion CCD.
conservative_rescaling: float = 0.8¶: Conservative rescaling value used to avoid taking steps exactly to impact.

Returns:¶

If the candidate had a collision over the time interval. Computed time of impact (normalized).

Return type:¶

Tuple of

print_ccd_query(self, V0: numpy.ndarray[numpy.float64[m, n]], V1: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → None¶

class ipctk.VertexVertexCandidate¶

compute_distance(self, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → float¶

compute_distance_gradient(self, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → numpy.ndarray[numpy.float64[m, 1]]¶

compute_distance_hessian(self, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → numpy.ndarray[numpy.float64[m, n]]¶

num_vertices(self) → int¶

vertex_indices(self, E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → list[int[4]]¶

Get the indices of the vertices

Parameters:¶

E: numpy.ndarray[numpy.int32[m, n]]¶: edge matrix of mesh
F: numpy.ndarray[numpy.int32[m, n]]¶: face matrix of mesh

Returns:¶

List of vertex indices

class ipctk.EdgeVertexCandidate¶

Perform narrow-phase CCD on the candidate.

Parameters:¶

V0: numpy.ndarray[numpy.float64[m, n]]¶: Mesh vertex positions at the start of the time step.
V1: numpy.ndarray[numpy.float64[m, n]]¶: Mesh vertex positions at the end of the time step.
E: numpy.ndarray[numpy.int32[m, n]]¶: Mesh edges as rows of indicies into V.
F: numpy.ndarray[numpy.int32[m, n]]¶: Mesh triangular faces as rows of indicies into V.
tmax: float = 1.0¶: Maximum time (normalized) to look for collisions. Should be in [0, 1].
tolerance: float = 1e-06¶: CCD tolerance used by Tight-Inclusion CCD.
max_iterations: int = 10000000¶: Maximum iterations used by Tight-Inclusion CCD.
conservative_rescaling: float = 0.8¶: Conservative rescaling value used to avoid taking steps exactly to impact.

Returns:¶

If the candidate had a collision over the time interval. Computed time of impact (normalized).

Return type:¶

Tuple of

compute_distance(self: ipctk.EdgeVertexCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.PointEdgeDistanceType = <PointEdgeDistanceType.AUTO: 3>) → float¶

compute_distance_gradient(self: ipctk.EdgeVertexCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.PointEdgeDistanceType = <PointEdgeDistanceType.AUTO: 3>) → numpy.ndarray[numpy.float64[m, 1]]¶

compute_distance_hessian(self: ipctk.EdgeVertexCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.PointEdgeDistanceType = <PointEdgeDistanceType.AUTO: 3>) → numpy.ndarray[numpy.float64[m, n]]¶

num_vertices(self) → int¶

print_ccd_query(self, V0: numpy.ndarray[numpy.float64[m, n]], V1: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → None¶

vertex_indices(self, E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → list[int[4]]¶

class ipctk.EdgeEdgeCandidate¶

Perform narrow-phase CCD on the candidate.

Parameters:¶

V0: numpy.ndarray[numpy.float64[m, n]]¶: Mesh vertex positions at the start of the time step.
V1: numpy.ndarray[numpy.float64[m, n]]¶: Mesh vertex positions at the end of the time step.
E: numpy.ndarray[numpy.int32[m, n]]¶: Mesh edges as rows of indicies into V.
F: numpy.ndarray[numpy.int32[m, n]]¶: Mesh triangular faces as rows of indicies into V.
tmax: float = 1.0¶: Maximum time (normalized) to look for collisions. Should be in [0, 1].
tolerance: float = 1e-06¶: CCD tolerance used by Tight-Inclusion CCD.
max_iterations: int = 10000000¶: Maximum iterations used by Tight-Inclusion CCD.
conservative_rescaling: float = 0.8¶: Conservative rescaling value used to avoid taking steps exactly to impact.

Returns:¶

If the candidate had a collision over the time interval. Computed time of impact (normalized).

Return type:¶

Tuple of

compute_distance(self: ipctk.EdgeEdgeCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.EdgeEdgeDistanceType = <EdgeEdgeDistanceType.AUTO: 9>) → float¶

compute_distance_gradient(self: ipctk.EdgeEdgeCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.EdgeEdgeDistanceType = <EdgeEdgeDistanceType.AUTO: 9>) → numpy.ndarray[numpy.float64[m, 1]]¶

compute_distance_hessian(self: ipctk.EdgeEdgeCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.EdgeEdgeDistanceType = <EdgeEdgeDistanceType.AUTO: 9>) → numpy.ndarray[numpy.float64[m, n]]¶

num_vertices(self) → int¶

print_ccd_query(self, V0: numpy.ndarray[numpy.float64[m, n]], V1: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → None¶

vertex_indices(self, E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → list[int[4]]¶

class ipctk.EdgeFaceCandidate¶

class ipctk.FaceVertexCandidate¶

ccd(self, V0: numpy.ndarray[numpy.float64[m, n]], V1: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], min_distance: float = 0.0, tmax: float = 1.0, tolerance: float = 1e-06, max_iterations: int = 10000000, conservative_rescaling: float = 0.8) → tuple[bool, float]¶

compute_distance(self: ipctk.FaceVertexCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.PointTriangleDistanceType = <PointTriangleDistanceType.AUTO: 7>) → float¶

compute_distance_gradient(self: ipctk.FaceVertexCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.PointTriangleDistanceType = <PointTriangleDistanceType.AUTO: 7>) → numpy.ndarray[numpy.float64[m, 1]]¶

compute_distance_hessian(self: ipctk.FaceVertexCandidate, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]], dtype: ipctk.PointTriangleDistanceType = <PointTriangleDistanceType.AUTO: 7>) → numpy.ndarray[numpy.float64[m, n]]¶

num_vertices(self) → int¶

print_ccd_query(self, V0: numpy.ndarray[numpy.float64[m, n]], V1: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → None¶

vertex_indices(self, E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → list[int[4]]¶

class ipctk.Candidates¶

clear(self) → None¶

empty(self) → bool¶

save_obj(self, filename: str, V: numpy.ndarray[numpy.float64[m, n]], E: numpy.ndarray[numpy.int32[m, n]], F: numpy.ndarray[numpy.int32[m, n]]) → bool¶

CCD¶

Broad Phase¶

Candidates¶

Narrow Phase¶