README.md

I/O for mesh files.

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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, ANSYS msh, AVS-UCD, CGNS, DOLFIN XML, Exodus, FLAC3D, H5M, Kratos/MDPA, Medit, MED/Salome, Nastran (bulk data), Neuroglancer precomputed format, Gmsh (versions 2 and 4), OBJ, OFF, PERMAS, PLY, STL, TetGen .node/.ele, SVG (2D only, output only), UGRID, VTK, VTU (not raw binary data), WKT (TIN), XDMF.

Install with

pip install meshio[all]

and simply call

meshio-convert input.msh output.vtu

with any of the supported formats.

In Python, simply do

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
)
# mesh.points, mesh.cells, mesh.cells_dict, ...

# mesh.vtk.read() is also possible

to read a mesh. To write, do

points = numpy.array([
    [0.0, 0.0, 0.0],
    [0.0, 1.0, 0.0],
    [0.0, 0.0, 1.0],
    ])
cells = [
    ("triangle", numpy.array([[0, 1, 2]]))
]
meshio.write_points_cells(
    "foo.vtk",
    points,
    cells,
    # Optionally provide extra data on points, cells, etc.
    # point_data=point_data,
    # cell_data=cell_data,
    # field_data=field_data
    )

or explicitly create a mesh object for writing

mesh = meshio.Mesh(points, cells)
meshio.write(
    "foo.vtk",  # str, os.PathLike, or buffer/ open file
    mesh,
    # file_format="vtk",  # optional if first argument is a path; inferred from extension
)

# mesh.vtk.write() is also possible

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).

Reading and writing can also be handled directly by the Mesh object:

m = meshio.Mesh.read(filename, "vtk")  # same arguments as meshio.read
m.write("foo.vtk")  # same arguments as meshio.write, besides `mesh`

Time series

The XDMF format supports time series with a shared mesh. You can write times series data using meshio with

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

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)

Performance comparison

The comparisons here are for a tetrahedral mesh with about 400k points and 2M tetrahedra. The red lines mark the size of the mesh in memory.

File sizes

I/O speed

Maximum memory usage

Installation

meshio is available from the Python Package Index, so simply do

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:

conda install -c conda-forge meshio

Testing

To run the meshio unit tests, check out this repository and type

pytest

License

meshio is published under the MIT license.