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245 lines
9.9 KiB
245 lines
9.9 KiB
3 years ago
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Metadata-Version: 2.1
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Name: meshio
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Version: 5.0.2
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Summary: I/O for many mesh formats
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Home-page: https://github.com/nschloe/meshio
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Author: Nico Schlömer et al.
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Author-email: nico.schloemer@gmail.com
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License: MIT
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Project-URL: Code, https://github.com/nschloe/meshio
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Project-URL: Issues, https://github.com/nschloe/meshio/issues
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Project-URL: Funding, https://github.com/sponsors/nschloe
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Keywords: mesh,file formats,scientific,engineering,fem,finite elements
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Platform: UNKNOWN
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Science/Research
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.7
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Classifier: Programming Language :: Python :: 3.8
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Topic :: Scientific/Engineering
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Classifier: Topic :: Utilities
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Requires-Python: >=3.7
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Description-Content-Type: text/markdown
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License-File: LICENSE.txt
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Requires-Dist: numpy
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Requires-Dist: importlib-metadata ; python_version < "3.8"
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Provides-Extra: all
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Requires-Dist: netCDF4 ; extra == 'all'
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Requires-Dist: h5py ; extra == 'all'
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<p align="center">
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<a href="https://github.com/nschloe/meshio"><img alt="meshio" src="https://nschloe.github.io/meshio/logo-with-text.svg" width="60%"></a>
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<p align="center">I/O for mesh files.</p>
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</p>
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[](https://pypi.org/project/meshio)
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[](https://anaconda.org/conda-forge/meshio/)
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[](https://repology.org/project/python:meshio/versions)
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[](https://pypi.org/project/meshio/)
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[](https://doi.org/10.5281/zenodo.1173115)
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[](https://github.com/nschloe/meshio)
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[](https://pypistats.org/packages/meshio)
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[](https://discord.gg/Z6DMsJh4Hr)
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[](https://github.com/nschloe/meshio/actions?query=workflow%3Aci)
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[](https://app.codecov.io/gh/nschloe/meshio)
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[](https://lgtm.com/projects/g/nschloe/meshio)
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[](https://github.com/psf/black)
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There are various mesh formats available for representing unstructured meshes.
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meshio can read and write all of the following and smoothly converts between them:
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> [Abaqus](http://abaqus.software.polimi.it/v6.14/index.html) (`.inp`),
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ANSYS msh (`.msh`),
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[AVS-UCD](https://lanl.github.io/LaGriT/pages/docs/read_avs.html) (`.avs`),
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[CGNS](https://cgns.github.io/) (`.cgns`),
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[DOLFIN XML](https://manpages.ubuntu.com/manpages/disco/man1/dolfin-convert.1.html) (`.xml`),
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[Exodus](https://nschloe.github.io/meshio/exodus.pdf) (`.e`, `.exo`),
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[FLAC3D](https://www.itascacg.com/software/flac3d) (`.f3grid`),
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[H5M](https://www.mcs.anl.gov/~fathom/moab-docs/h5mmain.html) (`.h5m`),
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[Kratos/MDPA](https://github.com/KratosMultiphysics/Kratos/wiki/Input-data) (`.mdpa`),
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[Medit](https://people.sc.fsu.edu/~jburkardt/data/medit/medit.html) (`.mesh`, `.meshb`),
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[MED/Salome](https://docs.salome-platform.org/latest/dev/MEDCoupling/developer/med-file.html) (`.med`),
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[Nastran](https://help.autodesk.com/view/NSTRN/2019/ENU/?guid=GUID-42B54ACB-FBE3-47CA-B8FE-475E7AD91A00) (bulk data, `.bdf`, `.fem`, `.nas`),
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[Netgen](https://github.com/ngsolve/netgen) (`.vol`, `.vol.gz`),
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[Neuroglancer precomputed format](https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed#mesh-representation-of-segmented-object-surfaces),
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[Gmsh](https://gmsh.info/doc/texinfo/gmsh.html#File-formats) (format versions 2.2, 4.0, and 4.1, `.msh`),
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[OBJ](https://en.wikipedia.org/wiki/Wavefront_.obj_file) (`.obj`),
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[OFF](https://segeval.cs.princeton.edu/public/off_format.html) (`.off`),
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[PERMAS](https://www.intes.de) (`.post`, `.post.gz`, `.dato`, `.dato.gz`),
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[PLY](https://en.wikipedia.org/wiki/PLY_(file_format)) (`.ply`),
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[STL](https://en.wikipedia.org/wiki/STL_(file_format)) (`.stl`),
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[Tecplot .dat](http://paulbourke.net/dataformats/tp/),
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[TetGen .node/.ele](https://wias-berlin.de/software/tetgen/fformats.html),
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[SVG](https://www.w3.org/TR/SVG/) (2D output only) (`.svg`),
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[SU2](https://su2code.github.io/docs_v7/Mesh-File/) (`.su2`),
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[UGRID](https://www.simcenter.msstate.edu/software/documentation/ug_io/3d_grid_file_type_ugrid.html) (`.ugrid`),
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[VTK](https://vtk.org/wp-content/uploads/2015/04/file-formats.pdf) (`.vtk`),
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[VTU](https://vtk.org/Wiki/VTK_XML_Formats) (`.vtu`),
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[WKT](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry) ([TIN](https://en.wikipedia.org/wiki/Triangulated_irregular_network)) (`.wkt`),
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[XDMF](https://www.xdmf.org/index.php/XDMF_Model_and_Format) (`.xdmf`, `.xmf`).
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Install with
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```
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pip install meshio[all]
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```
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(`[all]` pulls in all optional dependencies. By default, meshio only uses numpy.)
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You can then use the command-line tool
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<!--pytest-codeblocks:skip-->
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```sh
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meshio convert input.msh output.vtk # convert between two formats
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meshio info input.xdmf # show some info about the mesh
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meshio compress input.vtu # compress the mesh file
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meshio decompress input.vtu # decompress the mesh file
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meshio binary input.msh # convert to binary format
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meshio ascii input.msh # convert to ASCII format
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```
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with any of the supported formats.
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In Python, simply do
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<!--pytest-codeblocks:skip-->
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```python
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import meshio
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mesh = meshio.read(
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filename, # string, os.PathLike, or a buffer/open file
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# file_format="stl", # optional if filename is a path; inferred from extension
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# see meshio-convert -h for all possible formats
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)
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# mesh.points, mesh.cells, mesh.cells_dict, ...
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# mesh.vtk.read() is also possible
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```
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to read a mesh. To write, do
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```python
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import meshio
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# two triangles and one quad
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points = [
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[0.0, 0.0],
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[1.0, 0.0],
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[0.0, 1.0],
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[1.0, 1.0],
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[2.0, 0.0],
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[2.0, 1.0],
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]
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cells = [
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("triangle", [[0, 1, 2], [1, 3, 2]]),
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("quad", [[1, 4, 5, 3]]),
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]
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mesh = meshio.Mesh(
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points,
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cells,
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# Optionally provide extra data on points, cells, etc.
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point_data={"T": [0.3, -1.2, 0.5, 0.7, 0.0, -3.0]},
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# Each item in cell data must match the cells array
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cell_data={"a": [[0.1, 0.2], [0.4]]},
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)
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mesh.write(
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"foo.vtk", # str, os.PathLike, or buffer/open file
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# file_format="vtk", # optional if first argument is a path; inferred from extension
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)
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# Alternative with the same options
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meshio.write_points_cells("foo.vtk", points, cells)
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```
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For both input and output, you can optionally specify the exact `file_format`
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(in case you would like to enforce ASCII over binary VTK, for example).
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#### Time series
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The [XDMF format](https://www.xdmf.org/index.php/XDMF_Model_and_Format) supports time
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series with a shared mesh. You can write times series data using meshio with
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<!--pytest-codeblocks:skip-->
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```python
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with meshio.xdmf.TimeSeriesWriter(filename) as writer:
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writer.write_points_cells(points, cells)
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for t in [0.0, 0.1, 0.21]:
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writer.write_data(t, point_data={"phi": data})
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```
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and read it with
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<!--pytest-codeblocks:skip-->
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```python
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with meshio.xdmf.TimeSeriesReader(filename) as reader:
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points, cells = reader.read_points_cells()
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for k in range(reader.num_steps):
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t, point_data, cell_data = reader.read_data(k)
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```
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### ParaView plugin
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<img alt="gmsh paraview" src="https://nschloe.github.io/meshio/gmsh-paraview.png" width="60%">
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*A Gmsh file opened with ParaView.*
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If you have downloaded a binary version of ParaView, you may proceed as follows.
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* Install meshio for the Python major version that ParaView uses (check `pvpython --version`)
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* Open ParaView
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* Find the file `paraview-meshio-plugin.py` of your meshio installation (on Linux:
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`~/.local/share/paraview-5.9/plugins/`) and load it under _Tools / Manage Plugins / Load New_
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* _Optional:_ Activate _Auto Load_
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You can now open all meshio-supported files in ParaView.
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### Performance comparison
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The comparisons here are for a triangular mesh with about 900k points and 1.8M
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triangles. The red lines mark the size of the mesh in memory.
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#### File sizes
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<img alt="file size" src="https://nschloe.github.io/meshio/filesizes.svg" width="60%">
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#### I/O speed
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<img alt="performance" src="https://nschloe.github.io/meshio/performance.svg" width="90%">
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#### Maximum memory usage
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<img alt="memory usage" src="https://nschloe.github.io/meshio/memory.svg" width="90%">
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### Installation
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meshio is [available from the Python Package Index](https://pypi.org/project/meshio/),
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so simply run
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```
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pip install meshio
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```
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to install.
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Additional dependencies (`netcdf4`, `h5py`) are required for some of the output formats
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and can be pulled in by
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```
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pip install meshio[all]
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```
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You can also install meshio from [Anaconda](https://anaconda.org/conda-forge/meshio):
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```
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conda install -c conda-forge meshio
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```
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### Testing
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To run the meshio unit tests, check out this repository and type
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```
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tox
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```
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### License
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meshio is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).
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