| Name | stl-reader JSON |
| Version |
0.2.2
JSON |
| download |
| home_page | None |
| Summary | Read in STL files |
| upload_time | 2024-11-26 21:27:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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|
############
stl-reader
############
|pypi| |MIT|
.. |pypi| image:: https://img.shields.io/pypi/v/stl-reader.svg?logo=python&logoColor=white
:target: https://pypi.org/project/stl-reader/
.. |MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg
:target: https://opensource.org/licenses/MIT
``stl-reader`` is a Python library for rapidly reading binary and ASCII
STL files. It wraps a `nanobind
<https://nanobind.readthedocs.io/en/latest/>`_ interface to the fast STL
library provided by `libstl <https://github.com/aki5/libstl>`_. Thanks
@aki5!
The main advantage of ``stl-reader`` over other STL reading libraries is
its performance. It is particularly well-suited for large files, mainly
due to its efficient use of hashing when merging points. This results in
a 5-35x speedup over VTK for files containing between 4,000 and
9,000,000 points.
See the benchmarks below for more details.
**************
Installation
**************
The recommended way to install ``stl-reader`` is via PyPI:
.. code:: sh
pip install stl-reader
You can also clone the repository and install it from source:
.. code:: sh
git clone https://github.com/pyvista/stl-reader.git
cd stl-reader
pip install .
*******
Usage
*******
Load in the vertices and indices of a STL file directly as a NumPy
array:
.. code:: pycon
>>> import stl_reader
>>> vertices, indices = stl_reader.read("example.stl")
>>> vertices
array([[-0.01671113, 0.5450843 , -0.8382146 ],
[ 0.01671113, 0.5450843 , -0.8382146 ],
[ 0. , 0.52573115, -0.8506509 ],
...,
[ 0.5952229 , -0.57455426, 0.56178033],
[ 0.56178033, -0.5952229 , 0.57455426],
[ 0.57455426, -0.56178033, 0.5952229 ]], dtype=float32)
>>> indices
array([[ 0, 1, 2],
[ 1, 3, 4],
[ 4, 5, 2],
...,
[9005998, 9005988, 9005999],
[9005999, 9005996, 9005995],
[9005998, 9005999, 9005995]], dtype=uint32)
In this example, ``vertices`` is a 2D NumPy array where each row
represents a vertex and the three columns represent the X, Y, and Z
coordinates, respectively. ``indices`` is a 1D NumPy array representing
the triangles from the STL file.
Alternatively, you can load in the STL file as a PyVista PolyData:
.. code:: pycon
>>> import stl_reader
>>> mesh = stl_reader.read_as_mesh('example.stl')
>>> mesh
PolyData (0x7f43063ec700)
N Cells: 1280000
N Points: 641601
N Strips: 0
X Bounds: -5.000e-01, 5.000e-01
Y Bounds: -5.000e-01, 5.000e-01
Z Bounds: -5.551e-17, 5.551e-17
N Arrays: 0
***********
Benchmark
***********
The main reason behind writing yet another STL file reader for Python is
to leverage the performant `libstl <https://github.com/aki5/libstl>`_
library.
Here are some timings from reading in a 1,000,000 point binary STL file:
+-------------+-----------------------+
| Library | Time (seconds) |
+=============+=======================+
| stl-reader | 0.174 |
+-------------+-----------------------+
| numpy-stl | 0.201 (see note) |
+-------------+-----------------------+
| PyVista | 1.663 |
| (VTK) | |
+-------------+-----------------------+
| meshio | 4.451 |
+-------------+-----------------------+
**Note** ``numpy-stl`` does not merge duplicate vertices.
Comparison with VTK
===================
Here's an additional benchmark comparing VTK with ``stl-reader``:
.. code:: python
import numpy as np
import time
import pyvista as pv
import matplotlib.pyplot as plt
import stl_reader
times = []
filename = 'tmp.stl'
for res in range(50, 800, 50):
mesh = pv.Plane(i_resolution=res, j_resolution=res).triangulate().subdivide(2)
mesh.save(filename)
tstart = time.time()
out_pv = pv.read(filename)
vtk_time = time.time() - tstart
tstart = time.time()
out_stl = stl_reader.read(filename)
stl_reader_time = time.time() - tstart
times.append([mesh.n_points, vtk_time, stl_reader_time])
print(times[-1])
times = np.array(times)
plt.figure(1)
plt.title('STL load time')
plt.plot(times[:, 0], times[:, 1], label='VTK')
plt.plot(times[:, 0], times[:, 2], label='stl_reader')
plt.xlabel('Number of Points')
plt.ylabel('Time to Load (seconds)')
plt.legend()
plt.figure(2)
plt.title('STL load time (Log-Log)')
plt.loglog(times[:, 0], times[:, 1], label='VTK')
plt.loglog(times[:, 0], times[:, 2], label='stl_reader')
plt.xlabel('Number of Points')
plt.ylabel('Time to Load (seconds)')
plt.legend()
plt.show()
.. image:: https://github.com/pyvista/stl-reader/raw/main/bench0.png
.. image:: https://github.com/pyvista/stl-reader/raw/main/bench1.png
Read in ASCII Meshes
====================
The `stl-reader` also supports ASCII files and is around 2.4 times
faster than VTK at reading ASCII files.
.. code:: python
import time
import stl_reader
import pyvista as pv
import numpy as np
# create and save a ASCII file
n = 1000
mesh = pv.Plane(i_resolution=n, j_resolution=n).triangulate()
mesh.save("/tmp/tmp-ascii.stl", binary=False)
# stl reader
tstart = time.time()
mesh = stl_reader.read_as_mesh("/tmp/tmp-ascii.stl")
print("stl-reader ", time.time() - tstart)
tstart = time.time()
pv_mesh = pv.read("/tmp/tmp-ascii.stl")
print("pyvista reader", time.time() - tstart)
# verify meshes are identical
assert np.allclose(mesh.points, pv_mesh.points)
# approximate time to read in the 1M point file:
# stl-reader 0.80303955078125
# pyvista reader 1.916085958480835
*****************************
License and Acknowledgments
*****************************
This project relies on `libstl <https://github.com/aki5/libstl>`_ for
reading in and merging the vertices of a STL file. Wherever code is
reused, the original `MIT License
<https://github.com/aki5/libstl/blob/master/LICENSE>`_ is mentioned.
The work in this repository is also licensed under the MIT License.
*********
Support
*********
If you are having issues, please feel free to raise an `Issue
<https://github.com/pyvista/stl-reader/issues>`_.
Raw data
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"description": "############\n stl-reader\n############\n\n|pypi| |MIT|\n\n.. |pypi| image:: https://img.shields.io/pypi/v/stl-reader.svg?logo=python&logoColor=white\n :target: https://pypi.org/project/stl-reader/\n\n.. |MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg\n :target: https://opensource.org/licenses/MIT\n\n``stl-reader`` is a Python library for rapidly reading binary and ASCII\nSTL files. It wraps a `nanobind\n<https://nanobind.readthedocs.io/en/latest/>`_ interface to the fast STL\nlibrary provided by `libstl <https://github.com/aki5/libstl>`_. Thanks\n@aki5!\n\nThe main advantage of ``stl-reader`` over other STL reading libraries is\nits performance. It is particularly well-suited for large files, mainly\ndue to its efficient use of hashing when merging points. This results in\na 5-35x speedup over VTK for files containing between 4,000 and\n9,000,000 points.\n\nSee the benchmarks below for more details.\n\n**************\n Installation\n**************\n\nThe recommended way to install ``stl-reader`` is via PyPI:\n\n.. code:: sh\n\n pip install stl-reader\n\nYou can also clone the repository and install it from source:\n\n.. code:: sh\n\n git clone https://github.com/pyvista/stl-reader.git\n cd stl-reader\n pip install .\n\n*******\n Usage\n*******\n\nLoad in the vertices and indices of a STL file directly as a NumPy\narray:\n\n.. code:: pycon\n\n >>> import stl_reader\n >>> vertices, indices = stl_reader.read(\"example.stl\")\n >>> vertices\n array([[-0.01671113, 0.5450843 , -0.8382146 ],\n [ 0.01671113, 0.5450843 , -0.8382146 ],\n [ 0. , 0.52573115, -0.8506509 ],\n ...,\n [ 0.5952229 , -0.57455426, 0.56178033],\n [ 0.56178033, -0.5952229 , 0.57455426],\n [ 0.57455426, -0.56178033, 0.5952229 ]], dtype=float32)\n >>> indices\n array([[ 0, 1, 2],\n [ 1, 3, 4],\n [ 4, 5, 2],\n ...,\n [9005998, 9005988, 9005999],\n [9005999, 9005996, 9005995],\n [9005998, 9005999, 9005995]], dtype=uint32)\n\nIn this example, ``vertices`` is a 2D NumPy array where each row\nrepresents a vertex and the three columns represent the X, Y, and Z\ncoordinates, respectively. ``indices`` is a 1D NumPy array representing\nthe triangles from the STL file.\n\nAlternatively, you can load in the STL file as a PyVista PolyData:\n\n.. code:: pycon\n\n >>> import stl_reader\n >>> mesh = stl_reader.read_as_mesh('example.stl')\n >>> mesh\n PolyData (0x7f43063ec700)\n N Cells: 1280000\n N Points: 641601\n N Strips: 0\n X Bounds: -5.000e-01, 5.000e-01\n Y Bounds: -5.000e-01, 5.000e-01\n Z Bounds: -5.551e-17, 5.551e-17\n N Arrays: 0\n\n***********\n Benchmark\n***********\n\nThe main reason behind writing yet another STL file reader for Python is\nto leverage the performant `libstl <https://github.com/aki5/libstl>`_\nlibrary.\n\nHere are some timings from reading in a 1,000,000 point binary STL file:\n\n+-------------+-----------------------+\n| Library | Time (seconds) |\n+=============+=======================+\n| stl-reader | 0.174 |\n+-------------+-----------------------+\n| numpy-stl | 0.201 (see note) |\n+-------------+-----------------------+\n| PyVista | 1.663 |\n| (VTK) | |\n+-------------+-----------------------+\n| meshio | 4.451 |\n+-------------+-----------------------+\n\n**Note** ``numpy-stl`` does not merge duplicate vertices.\n\nComparison with VTK\n===================\n\nHere's an additional benchmark comparing VTK with ``stl-reader``:\n\n.. code:: python\n\n import numpy as np\n import time\n import pyvista as pv\n import matplotlib.pyplot as plt\n import stl_reader\n\n times = []\n filename = 'tmp.stl'\n for res in range(50, 800, 50):\n mesh = pv.Plane(i_resolution=res, j_resolution=res).triangulate().subdivide(2)\n mesh.save(filename)\n\n tstart = time.time()\n out_pv = pv.read(filename)\n vtk_time = time.time() - tstart\n\n tstart = time.time()\n out_stl = stl_reader.read(filename)\n stl_reader_time = time.time() - tstart\n\n times.append([mesh.n_points, vtk_time, stl_reader_time])\n print(times[-1])\n\n\n times = np.array(times)\n plt.figure(1)\n plt.title('STL load time')\n plt.plot(times[:, 0], times[:, 1], label='VTK')\n plt.plot(times[:, 0], times[:, 2], label='stl_reader')\n plt.xlabel('Number of Points')\n plt.ylabel('Time to Load (seconds)')\n plt.legend()\n\n plt.figure(2)\n plt.title('STL load time (Log-Log)')\n plt.loglog(times[:, 0], times[:, 1], label='VTK')\n plt.loglog(times[:, 0], times[:, 2], label='stl_reader')\n plt.xlabel('Number of Points')\n plt.ylabel('Time to Load (seconds)')\n plt.legend()\n plt.show()\n\n.. image:: https://github.com/pyvista/stl-reader/raw/main/bench0.png\n\n.. image:: https://github.com/pyvista/stl-reader/raw/main/bench1.png\n\nRead in ASCII Meshes\n====================\n\nThe `stl-reader` also supports ASCII files and is around 2.4 times\nfaster than VTK at reading ASCII files.\n\n.. code:: python\n\n import time\n import stl_reader\n import pyvista as pv\n import numpy as np\n\n # create and save a ASCII file\n n = 1000\n mesh = pv.Plane(i_resolution=n, j_resolution=n).triangulate()\n mesh.save(\"/tmp/tmp-ascii.stl\", binary=False)\n\n # stl reader\n tstart = time.time()\n mesh = stl_reader.read_as_mesh(\"/tmp/tmp-ascii.stl\")\n print(\"stl-reader \", time.time() - tstart)\n\n tstart = time.time()\n pv_mesh = pv.read(\"/tmp/tmp-ascii.stl\")\n print(\"pyvista reader\", time.time() - tstart)\n\n # verify meshes are identical\n assert np.allclose(mesh.points, pv_mesh.points)\n\n # approximate time to read in the 1M point file:\n # stl-reader 0.80303955078125\n # pyvista reader 1.916085958480835\n\n*****************************\n License and Acknowledgments\n*****************************\n\nThis project relies on `libstl <https://github.com/aki5/libstl>`_ for\nreading in and merging the vertices of a STL file. Wherever code is\nreused, the original `MIT License\n<https://github.com/aki5/libstl/blob/master/LICENSE>`_ is mentioned.\n\nThe work in this repository is also licensed under the MIT License.\n\n*********\n Support\n*********\n\nIf you are having issues, please feel free to raise an `Issue\n<https://github.com/pyvista/stl-reader/issues>`_.\n",
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