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<p align="center">
<a href="https://github.com/nschloe/meshio"><img alt="meshio" src="https://nschloe.github.io/meshio/logo-with-text.svg" width="60%"></a>
<p align="center">I/O for mesh files.</p>
</p>
[](https://pypi.org/project/meshio/)
[](https://anaconda.org/conda-forge/meshio/)
[](https://repology.org/project/python:meshio/versions)
[](https://pypi.org/project/meshio/)
[](https://doi.org/10.5281/zenodo.1173115)
[](https://github.com/nschloe/meshio)
[](https://pepy.tech/project/meshio)
<!--[](https://pypistats.org/packages/meshio)-->
[](https://discord.gg/Z6DMsJh4Hr)
[](https://github.com/nschloe/meshio/actions?query=workflow%3Aci)
[](https://app.codecov.io/gh/nschloe/meshio)
[](https://lgtm.com/projects/g/nschloe/meshio)
[](https://github.com/psf/black)
There are various mesh formats available for representing unstructured meshes.
meshio can read and write all of the following and smoothly converts between them:
> [Abaqus](http://abaqus.software.polimi.it/v6.14/index.html) (`.inp`),
> ANSYS msh (`.msh`),
> [AVS-UCD](https://lanl.github.io/LaGriT/pages/docs/read_avs.html) (`.avs`),
> [CGNS](https://cgns.github.io/) (`.cgns`),
> [DOLFIN XML](https://manpages.ubuntu.com/manpages/jammy/en/man1/dolfin-convert.1.html) (`.xml`),
> [Exodus](https://nschloe.github.io/meshio/exodus.pdf) (`.e`, `.exo`),
> [FLAC3D](https://www.itascacg.com/software/flac3d) (`.f3grid`),
> [H5M](https://www.mcs.anl.gov/~fathom/moab-docs/h5mmain.html) (`.h5m`),
> [Kratos/MDPA](https://github.com/KratosMultiphysics/Kratos/wiki/Input-data) (`.mdpa`),
> [Medit](https://people.sc.fsu.edu/~jburkardt/data/medit/medit.html) (`.mesh`, `.meshb`),
> [MED/Salome](https://docs.salome-platform.org/latest/dev/MEDCoupling/developer/med-file.html) (`.med`),
> [Nastran](https://help.autodesk.com/view/NSTRN/2019/ENU/?guid=GUID-42B54ACB-FBE3-47CA-B8FE-475E7AD91A00) (bulk data, `.bdf`, `.fem`, `.nas`),
> [Netgen](https://github.com/ngsolve/netgen) (`.vol`, `.vol.gz`),
> [Neuroglancer precomputed format](https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed#mesh-representation-of-segmented-object-surfaces),
> [Gmsh](https://gmsh.info/doc/texinfo/gmsh.html#File-formats) (format versions 2.2, 4.0, and 4.1, `.msh`),
> [OBJ](https://en.wikipedia.org/wiki/Wavefront_.obj_file) (`.obj`),
> [OFF](https://segeval.cs.princeton.edu/public/off_format.html) (`.off`),
> [PERMAS](https://www.intes.de) (`.post`, `.post.gz`, `.dato`, `.dato.gz`),
> [PLY](<https://en.wikipedia.org/wiki/PLY_(file_format)>) (`.ply`),
> [STL](<https://en.wikipedia.org/wiki/STL_(file_format)>) (`.stl`),
> [Tecplot .dat](http://paulbourke.net/dataformats/tp/),
> [TetGen .node/.ele](https://wias-berlin.de/software/tetgen/fformats.html),
> [SVG](https://www.w3.org/TR/SVG/) (2D output only) (`.svg`),
> [SU2](https://su2code.github.io/docs_v7/Mesh-File/) (`.su2`),
> [UGRID](https://www.simcenter.msstate.edu/software/documentation/ug_io/3d_grid_file_type_ugrid.html) (`.ugrid`),
> [VTK](https://vtk.org/wp-content/uploads/2015/04/file-formats.pdf) (`.vtk`),
> [VTU](https://vtk.org/Wiki/VTK_XML_Formats) (`.vtu`),
> [WKT](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry) ([TIN](https://en.wikipedia.org/wiki/Triangulated_irregular_network)) (`.wkt`),
> [XDMF](https://xdmf.org/index.php/XDMF_Model_and_Format) (`.xdmf`, `.xmf`).
([Here's a little survey](https://forms.gle/PSeNb3N3gv3wbEus8) on which formats are actually
used.)
Install with one of
```
pip install meshio[all]
conda install -c conda-forge meshio
```
(`[all]` pulls in all optional dependencies. By default, meshio only uses numpy.)
You can then use the command-line tool
<!--pytest-codeblocks:skip-->
```sh
meshio convert input.msh output.vtk # convert between two formats
meshio info input.xdmf # show some info about the mesh
meshio compress input.vtu # compress the mesh file
meshio decompress input.vtu # decompress the mesh file
meshio binary input.msh # convert to binary format
meshio ascii input.msh # convert to ASCII format
```
with any of the supported formats.
In Python, simply do
<!--pytest-codeblocks:skip-->
```python
import meshio
mesh = meshio.read(
filename, # string, os.PathLike, or a buffer/open file
# file_format="stl", # optional if filename is a path; inferred from extension
# see meshio-convert -h for all possible formats
)
# mesh.points, mesh.cells, mesh.cells_dict, ...
# mesh.vtk.read() is also possible
```
to read a mesh. To write, do
```python
import meshio
# two triangles and one quad
points = [
[0.0, 0.0],
[1.0, 0.0],
[0.0, 1.0],
[1.0, 1.0],
[2.0, 0.0],
[2.0, 1.0],
]
cells = [
("triangle", [[0, 1, 2], [1, 3, 2]]),
("quad", [[1, 4, 5, 3]]),
]
mesh = meshio.Mesh(
points,
cells,
# Optionally provide extra data on points, cells, etc.
point_data={"T": [0.3, -1.2, 0.5, 0.7, 0.0, -3.0]},
# Each item in cell data must match the cells array
cell_data={"a": [[0.1, 0.2], [0.4]]},
)
mesh.write(
"foo.vtk", # str, os.PathLike, or buffer/open file
# file_format="vtk", # optional if first argument is a path; inferred from extension
)
# Alternative with the same options
meshio.write_points_cells("foo.vtk", points, cells)
```
For both input and output, you can optionally specify the exact `file_format`
(in case you would like to enforce ASCII over binary VTK, for example).
#### Time series
The [XDMF format](https://xdmf.org/index.php/XDMF_Model_and_Format) supports
time series with a shared mesh. You can write times series data using meshio
with
<!--pytest-codeblocks:skip-->
```python
with meshio.xdmf.TimeSeriesWriter(filename) as writer:
writer.write_points_cells(points, cells)
for t in [0.0, 0.1, 0.21]:
writer.write_data(t, point_data={"phi": data})
```
and read it with
<!--pytest-codeblocks:skip-->
```python
with meshio.xdmf.TimeSeriesReader(filename) as reader:
points, cells = reader.read_points_cells()
for k in range(reader.num_steps):
t, point_data, cell_data = reader.read_data(k)
```
### ParaView plugin
<img alt="gmsh paraview" src="https://nschloe.github.io/meshio/gmsh-paraview.png" width="60%">
*A Gmsh file opened with ParaView.*
If you have downloaded a binary version of ParaView, you may proceed as follows.
- Install meshio for the Python major version that ParaView uses (check `pvpython --version`)
- Open ParaView
- Find the file `paraview-meshio-plugin.py` of your meshio installation (on Linux:
`~/.local/share/paraview-5.9/plugins/`) and load it under _Tools / Manage Plugins / Load New_
- _Optional:_ Activate _Auto Load_
You can now open all meshio-supported files in ParaView.
### Performance comparison
The comparisons here are for a triangular mesh with about 900k points and 1.8M
triangles. The red lines mark the size of the mesh in memory.
#### File sizes
<img alt="file size" src="https://nschloe.github.io/meshio/filesizes.svg" width="60%">
#### I/O speed
<img alt="performance" src="https://nschloe.github.io/meshio/performance.svg" width="90%">
#### Maximum memory usage
<img alt="memory usage" src="https://nschloe.github.io/meshio/memory.svg" width="90%">
### Installation
meshio is [available from the Python Package Index](https://pypi.org/project/meshio/),
so simply run
```
pip install meshio
```
to install.
Additional dependencies (`netcdf4`, `h5py`) are required for some of the output formats
and can be pulled in by
```
pip install meshio[all]
```
You can also install meshio from [Anaconda](https://anaconda.org/conda-forge/meshio):
```
conda install -c conda-forge meshio
```
### Testing
To run the meshio unit tests, check out this repository and type
```
tox
```
### License
meshio is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).
|
