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Torch Delaunay - The Delaunay triangulation library for PyTorch

This is a fast library for computing Delaunay triangulation of 2-dimensional points.

The implementation is based on a sweep-algorithm, introduced by David Sinclair1 and later improved by Volodymyr Agafonkin2.

Here is an example of triangulation output produced by Torch Delaunay library for a random set of points: example

Installation

The library is distributed as PyPI package, to install the package, execute the following command:

pip install torch_delaunay

You can use the torch_delaunay library for a fast computation of Delaunay triangulation for points defined as PyTorch tensors.

Usage

import torch
from torch_delaunay.functional import shull2d
import matplotlib.pyplot as plt

# Compute Delaunay triangulation for randomly-generated 2-dimensional points.
points = torch.rand((100, 2))
simplices = shull2d(points)

# Plot the result
plt.triplot(points[:, 0], points[:, 1], triangles=simplices)
plt.title("Delaunay Triangulation")

Benchmarks

Benchmarks are collected on Apple M1 Pro chip with 32 GiB of memory.

Prob. Distribution Data Type Samples Time CPU Iterations
Uniform Float64 10000 4.18 ms 4.11 ms 171
Uniform Float64 100000 49.6 ms 48.3 ms 15
Uniform Float64 1000000 600 ms 592 ms 1
Uniform Float32 10000 4.37 ms 4.29 ms 161
Uniform Float32 100000 47.4 ms 46.4 ms 15
Uniform Float32 1000000 573 ms 568 ms 1
Normal Float64 10000 4.60 ms 4.49 ms 156
Normal Float64 100000 48.5 ms 47.4 ms 15
Normal Float64 1000000 568 ms 563 ms 1
Normal Float32 10000 4.42 ms 4.33 ms 165
Normal Float32 100000 46.5 ms 45.4 ms 15
Normal Float32 1000000 550 ms 547 ms 1

License

The Torch Delaunay is distributed under GPLv3 license. See the LICENSE file for full license text.

  1. David Sinclair - S-hull: a fast radial sweep-hull routine for Delaunay triangulation.

  2. Volodymyr Agafonkin - Delaunator: An incredibly fast and robust JavaScript library for Delaunay triangulation of 2D points.

Release files for torch-delaunay 1.3.0

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Table of built distributions (wheels) for torch-delaunay 1.3.0
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torch_delaunay-1.3.0-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
torch_delaunay-1.3.0-cp312-cp312-manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.28+ x86-64 Details
torch_delaunay-1.3.0-cp312-cp312-manylinux_2_28_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.28+ ARM64 Details
torch_delaunay-1.3.0-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
torch_delaunay-1.3.0-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
torch_delaunay-1.3.0-cp311-cp311-manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64 Details
torch_delaunay-1.3.0-cp311-cp311-manylinux_2_28_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ ARM64 Details
torch_delaunay-1.3.0-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
torch_delaunay-1.3.0-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
torch_delaunay-1.3.0-cp310-cp310-manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64 Details
torch_delaunay-1.3.0-cp310-cp310-manylinux_2_28_aarch64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ ARM64 Details
torch_delaunay-1.3.0-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
torch_delaunay-1.3.0-cp39-cp39-win_amd64.whl CPython 3.9 CPython 3.9 Windows x86-64 Details
torch_delaunay-1.3.0-cp39-cp39-manylinux_2_28_x86_64.whl CPython 3.9 CPython 3.9 Linux glibc 2.28+ x86-64 Details
torch_delaunay-1.3.0-cp39-cp39-manylinux_2_28_aarch64.whl CPython 3.9 CPython 3.9 Linux glibc 2.28+ ARM64 Details
torch_delaunay-1.3.0-cp39-cp39-macosx_11_0_arm64.whl CPython 3.9 CPython 3.9 macOS 11.0+ ARM64 Details

Total release size: 66.0 MB

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