tetgen python wrapper for points, PLCs and tetmesh inputs
Project description
tetgenpy
tetgenpy is a python wrapper for Hang Si's TetGen - A Quality Tetrahedral Mesh Generator and a 3D Delaunay Triangulator.
It helps to prepare various types of inputs - points, piecewise linear complexes (PLCs), and tetmesh - for tetrahedron mesh generation based on simple python types, such as list
and numpy.ndarray
.
Install
pip install tetgenpy
For current development version,
pip install git+https://github.com/tataratat/tetgenpy.git@main
Quick Start
Following is an example for tetrahedralization of a unit cube defined as PLCs.
Alternatively, you could also use tetgenpy.PLC class to prepare TetgenIO
.
import tetgenpy
import numpy as np
# tetrahedralize unit cube
# define points
points=[
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[1.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 0.0, 1.0],
[0.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
# define facets - it can be list of polygons.
# here, they are hexa faces
facets = [
[1, 0, 2, 3],
[0, 1, 5, 4],
[2, 0, 4, 6],
[1, 3, 7, 5],
[3, 2, 6, 7],
[4, 5, 7, 6],
]
# prepare TetgenIO - input for tetgen
tetgen_in = tetgenpy.TetgenIO()
# set points, facets, and facet_markers.
# facet_markers can be useful for setting boundary conditions
tetgen_in.setup_plc(
points=points,
facets=facets,
facet_markers=[[i] for i in range(1, len(facets) + 1)],
)
# tetgen's tetraheralize function with switches
tetgen_out = tetgenpy.tetrahedralize("qa.05", tetgen_in)
# unpack output
print(tetgen_out.points())
print(tetgen_out.tetrahedra())
print(tetgen_out.trifaces())
print(tetgen_out.trifacemarkers())
This package also provides access to tetgen's binary executable. Try:
$ tetgen -h
Dependencies
c++
python
build
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MD5 | ce6e16c36412db371741c6ab83547f6e |
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BLAKE2b-256 | 866addb4440a308279d76e7478d34fd9e4ef07d854c26935414d45473b236bde |
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Hashes for tetgenpy-0.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 97a8bb6ee6fcdbc1f938b8a76a10870aaffe95c8c041cdf180cc39ecee95c652 |
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MD5 | d1176e538149487a8aebb2b45cea0bb2 |
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BLAKE2b-256 | 4a3181b75be6cd6c24c1dd5ccf3c7966ce1c10401d4bd96de17640f4c5fce602 |
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Hashes for tetgenpy-0.0.1-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
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SHA256 | 10858cda0ab5d8aa1ba3e196e15285a307b3aecc15f1b190613b3c7b2475191f |
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MD5 | 031fab5394ffc6aa48a465d11644cfa5 |
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BLAKE2b-256 | 9211081070f66818d2567119d0dc491541a99e3462b13fa4bb35c4a2a8a25c71 |
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Hashes for tetgenpy-0.0.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | ac027b166df69b95b5dc0768179c113052d1d68c24256b6c72ca88787b189e67 |
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MD5 | 87b9eeea072368a76b79eb0ed2b2cfe7 |
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BLAKE2b-256 | a97633fb7c7620faa635275aa7d56dcbe84aafc0836dd411a53380d5d28716e7 |
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Hashes for tetgenpy-0.0.1-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | 64b7e66b13d6b3bcee4c9a4b98ecb1182793e14d9738ab505bcfc33ad46c889e |
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MD5 | b8cbe8b8b0b28161f8012c087e9c46c4 |
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BLAKE2b-256 | 22c6d25f00402dcaffbf83a4299e8f5558aea71229f482a8eea3c42925eec9b8 |
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Hashes for tetgenpy-0.0.1-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
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SHA256 | 6477ebb67346deb48b9f05da1344cc6ed1bfe1e832093ae86b6d05118ee48db3 |
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MD5 | 592db2e0c3dcf714a3db9b4bf707371e |
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BLAKE2b-256 | 9b77050e0e292d2d7988adfe6a56871f50b2355f8e87c5ebe5a2156dd15f2c59 |
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Hashes for tetgenpy-0.0.1-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | c29d158273505fdac403d3757d878b2d99de812e2a3c4ae7cdbd18b4c22658b2 |
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MD5 | e4c5e4f674048cd321f8354da5da6be1 |
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BLAKE2b-256 | ac93d4313a8771dc8e0a55762f99e39db4e6d4b7d66edcb946b4beacc14446ea |
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Hashes for tetgenpy-0.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 898da435c52c624c228cb99f8e42197354f9df7da5b854cc8a9349144399139d |
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MD5 | da255450113135545a74d79b6281387f |
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BLAKE2b-256 | 2cc69c903a601d33b8e261039a30c2d9f142f47a429d3fade30a9e8c9730008f |
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Hashes for tetgenpy-0.0.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
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SHA256 | 1721b099ff3d23829f91b1d02cc45cacaf020a28251b54d852f49f3b802d1893 |
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MD5 | 8f7bd07aa0b0ae6d17745057f1c56068 |
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BLAKE2b-256 | 7b3a72c3a92e8c66a80f8a230d22a1800443b55bbbcf4cb2f2a3dc80a2e51673 |
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Hashes for tetgenpy-0.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 4c8ed8dbc72cb489806c01d1cc79d054b279fc15df854741aaddc5b1f8c50568 |
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MD5 | c6a6dfee11f4aee986f566a08a19db41 |
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BLAKE2b-256 | 3b010ec6a69b328c0e2ebc8b0ba9550a679cd6379c83880d0fd5fd8e2555e391 |
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Hashes for tetgenpy-0.0.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 3936833a7da88a15aea9e794375ff888742e04b027e91a2db3c0b8713baa0bd8 |
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MD5 | 8a2f2cba5e7dd5535431eb8a367fa1b2 |
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BLAKE2b-256 | 723ef069b0e64c10f06aefe3468d07f5ed42edb316fd1f996e6c8848f0d7c9a8 |
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Hashes for tetgenpy-0.0.1-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3632cedea03bc287ad214a77158a21c7aea5fda27aa094b7c1cfe1f056704322 |
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MD5 | 18b1dd78a1d69a2227ecd1f8659b1b2d |
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BLAKE2b-256 | 71190a86788a8d3545797e83d89e6164b18b4184d9557f237358ee250b6ef1f7 |
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Hashes for tetgenpy-0.0.1-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
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SHA256 | 3123f4d49745a472ce3120ceb313ab522b1aaef9758e974f0a72720981fb33d8 |
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MD5 | abe12f87571aed74703b853e5a7dda8e |
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BLAKE2b-256 | c7281752963f1ec2d06864a8e13936ce1214f91aca66354e0ecb15832e5b4146 |
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Hashes for tetgenpy-0.0.1-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 9e56e47c49fa1901188bf8ca66d72ccf2a5cd1a7e9adbbc48ca225ca1e1a0f3f |
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MD5 | 54457a2d6fadc7be21ad1f11c0544014 |
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BLAKE2b-256 | 2925ed2c8a33a9fefc3ead6aa6883487a6fb3c9bdeda2499cbdcea427707a4ed |
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Hashes for tetgenpy-0.0.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 49bfb6ed28f2e273e38a968c2110f55c1a99eb84849ce4804d05f582cb3c2c78 |
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MD5 | b6239582863fdadba812caec13c40495 |
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BLAKE2b-256 | d7a79af06b9a8efb4f241bbc6712a63268ff7965f3ad17cd9307bffe343f94f3 |
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Hashes for tetgenpy-0.0.1-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
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SHA256 | e2481b6e3b7c39f46ac3b1401650283d12693426f57b3a2c5e7b4eb1b12b5a48 |
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MD5 | f6ded8d7bf519c3360ddf1dcc8b41a89 |
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BLAKE2b-256 | 1d79a7b3366713585a791139aafb7a3006ee0c859f371536f489e11a0879c726 |
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Hashes for tetgenpy-0.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 9fe109c0fa0f2b4f11ec3fc6489559a0a47fbf6968d42d8ab01246cbb1a2d6d6 |
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MD5 | f8c621960011a46263cfe7584d4843c1 |
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BLAKE2b-256 | 047b112e6a5c731adf7c8afa87f1038daaaea6b8a7b2363b9e4a4d6ef0fbf575 |
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Hashes for tetgenpy-0.0.1-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0fd0bf3d027f6e0f7d02cece5b52e602738646b992211e505942aede5d1f240e |
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MD5 | 49a3093d83950929185bcb763056627f |
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BLAKE2b-256 | abf9b8e4d05fe466ebd2c93bfc5994381ac03e403f41aabd34382584dc77e628 |