meshio

To help you get started, we've collected the most common ways that meshio is being used within popular public projects.

nschloe / meshio / meshio / stl / _stl.py View on Github

# )

    # numpy.loadtxt is super slow
    # data = numpy.loadtxt(
    #     f,
    #     comments=["solid", "facet", "outer loop", "endloop", "endfacet", "endsolid"],
    #     usecols=(1, 2, 3),
    # )
    data = iter_loadtxt(
        f,
        comments=("solid", "outer loop", "endloop", "endfacet", "endsolid"),
        # usecols=(1, 2, 3),
    )

    if data.shape[0] % 4 != 0:
        raise ReadError()

    # split off the facet normals
    facet_rows = numpy.zeros(len(data), dtype=bool)
    facet_rows[0::4] = True
    facet_normals = data[facet_rows]
    data = data[~facet_rows]

    facets = numpy.split(data, data.shape[0] // 3)
    points, cells = data_from_facets(facets)
    return Mesh(points, cells, cell_data={"facet_normals": [facet_normals]})

View more ways to use meshio

I/O for mesh files.

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 (.inp), > ANSYS msh (.msh), > AVS-UCD (.avs), > CGNS (.cgns), > DOLFIN XML (.xml), > Exodus (.e, .exo), > FLAC3D (.f3grid), > H5M (.h5m), > Kratos/MDPA (.mdpa), > Medit (.mesh, .meshb), > MED/Salome (.med), > Nastran (bulk data, .bdf, .fem, .nas), > Netgen (.vol, .vol.gz), > Neuroglancer precomputed format, > Gmsh (format versions 2.2, 4.0, and 4.1, .msh), > OBJ (.obj), > OFF (.off), > PERMAS (.post, .post.gz, .dato, .dato.gz), > PLY (.ply), > STL (.stl), > Tecplot .dat, > TetGen .node/.ele, > SVG (2D output only) (.svg), > SU2 (.su2), > UGRID (.ugrid), > VTK (.vtk), > VTU (.vtu), > WKT (TIN) (.wkt), > XDMF (.xdmf, .xmf).

(Here's a little survey 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

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

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

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 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)

ParaView plugin

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

I/O speed

Maximum memory usage

Installation

meshio is available from the Python Package Index, 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:

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.