Particle Pairs¶

Particula-beta Index / Particula Beta / Lagrangian / Particle Pairs

Auto-generated documentation for particula_beta.lagrangian.particle_pairs module.

calculate_pairwise_distance¶

need to test this:

Calculate the pairwise Euclidean distances between points in a given position tensor.

This function computes the pairwise distances between points represented in the input tensor. Each row of the input tensor is considered a point in n-dimensional space.

Arguments¶

position torch.Tensor - A 2D tensor of shape [n_dimensions, n_points]

Returns¶

torch.Tensor - A 2D tensor of shape [n_points, n_points] containing the pairwise Euclidean distances between each pair of points. The element at [i, j] in the output tensor represents the distance between the i-th and j-th points in the input tensor.

Examples¶

position = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

Output will be a 3x3 tensor with the pairwise distances between these¶

3 points.

Signature¶

def calculate_pairwise_distance(position: torch.Tensor) -> torch.Tensor: ...

full_sweep_and_prune¶

Show source in particle_pairs.py:159

Sweep and prune algorithm for collision detection along all three axes (x, y, z). This function identifies pairs of particles that are close enough to potentially collide in 3D space.

Arguments¶

position torch.Tensor - The 2D tensor of particle positions, where each row represents an axis (x, y, z).
radius torch.Tensor - The radius of particles.

Returns¶

torch.Tensor - A tensor containing pairs of indices of potentially colliding particles.

Signature¶

def full_sweep_and_prune(
    position: torch.Tensor, radius: torch.Tensor
) -> torch.Tensor: ...

full_sweep_and_prune_simplified¶

Show source in particle_pairs.py:240

A simplified version of the full sweep and prune algorithm for collision written above, it is not working yet. there is an error in the update of the indices in the y and z axis.

Sweep and prune algorithm for collision detection along all three axes (x, y, z). This function identifies pairs of particles that are close enough to potentially collide in 3D space.

Arguments¶

position torch.Tensor - The 2D tensor of particle positions, where each row represents an axis (x, y, z).
radius torch.Tensor - The radius of particles.

Returns¶

torch.Tensor - A tensor containing pairs of indices of potentially colliding particles.

Signature¶

def full_sweep_and_prune_simplified(
    position: torch.Tensor, radius: torch.Tensor, working_yet: bool = False
) -> torch.Tensor: ...

remove_duplicates¶

Show source in particle_pairs.py:7

Removes duplicate entries from a specified column in a tensor of index pairs.

This function is designed to work with tensors where each row represents a pair of indices. It removes rows containing duplicate entries in the specified column.

Arguments¶

index_pairs (torch.Tensor): A 2D tensor of shape [n, 2], where n is the number of index pairs.
index_to_remove (int): The column index (0 or 1) from which to remove duplicate entries.

Returns¶

- torch.Tensor - A 2D tensor of index pairs with duplicates removed from the specified column.

Examples¶

index_pairs = torch.tensor([[1, 2], [3, 4], [1, 2]]) index_to_remove = 0

Output will be [[1, 2], [3, 4]] assuming column 0 is chosen for removing¶

duplicates.

Signature¶

def remove_duplicates(
    index_pairs: torch.Tensor, index_to_remove: int
) -> torch.Tensor: ...

single_axis_sweep_and_prune¶

Show source in particle_pairs.py:118

Sweep and prune algorithm for collision detection along a single axis. This function identifies pairs of particles that are close enough to potentially collide.

Arguments¶

position_axis torch.Tensor - The position of particles along a single axis.
radius torch.Tensor - The radius of particles.

Returns¶

Tuple[torch.Tensor, torch.Tensor] - Two tensors containing the indices of potentially colliding particles.

Signature¶

def single_axis_sweep_and_prune(
    position_axis: torch.Tensor, radius: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]: ...

validate_pair_distance¶

Show source in particle_pairs.py:79

Validates if the Euclidean distances between pairs of points are smaller than the sum of their radii.

Arguments¶

collision_indices_pairs torch.Tensor - A tensor containing pairs of indices of potentially colliding particles.
position torch.Tensor - A 2D tensor of particle positions, where each column represents a particle, and each row represents an axis.
radius torch.Tensor - A 1D tensor representing the radius of each particle.

Returns¶

torch.Tensor - A tensor containing the indices of the pairs of particles that are actually colliding.

Signature¶

def validate_pair_distance(
    collision_indices_pairs: torch.Tensor, position: torch.Tensor, radius: torch.Tensor
) -> torch.Tensor: ...