meshio++

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 (.inp), ANSYS msh (.msh), Ansys/APDL coded database (.cdb, .inp), AVS-UCD (.avs), CGNS (.cgns), DOLFIN XML (.xml), COMSOL (.mphtxt), Exodus (.e, .exo), EnSight Gold (geometry, .case/.geo), FLAC3D (.f3grid), FLUX (mesh .pf3, field .dex), FreeFem++ (.msh), H5M (.h5m), HMF (.hmf, experimental, meshio++-specific), I-deas Universal / UNV (.unv), ANSYS Fluent interpolation (.ip), Kratos/MDPA (.mdpa), Medit (.mesh, .meshb), MED/Salome (.med), Modulef (mesh .mfm, field .mff), 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), OpenFOAM polyMesh (.foam, read-only), PERMAS (.post, .post.gz, .dato, .dato.gz), PLY (.ply), STL (.stl), Tecplot .dat, TetGen .node/.ele, Triangle .node/.ele/.poly, SVG (output only; 2D direct, 3D via skin projection) (.svg), TikZ (LaTeX output only; 2D direct, 3D via skin projection) (.tikz), SU2 (.su2), UGRID (.ugrid), VTK (.vtk), VTP (.vtp), VTU (.vtu), WKT (TIN) (.wkt), XDMF (.xdmf, .xmf).

meshio++ ships a C++20 core (built with pybind11 + scikit-build-core) that reads and writes most formats with zero-copy numpy at the I/O boundary, plus optional HDF5/netCDF acceleration and a selectable parallel backend (AUTO by default — prefers OpenMP, then STL+TBB, then sequential; override with -DMESHIOPLUSPLUS_PARALLEL_BACKEND=...). Every format has a pure-Python fallback, so behaviour and file compatibility are identical whether or not the native libraries are present. For a standalone C++ build use build/configure.sh (Linux/macOS) or build/configure.bat (Windows). Full docs (install, data model, per-format options, CLI) live at the documentation site (sources under doc/).

Install with

pip install meshioplusplus[all]

([all] pulls in all optional dependencies. By default, meshio++ only uses numpy.) You can then use the command-line tool

meshioplusplus convert    input.msh output.vtk   # convert between two formats

meshioplusplus info       input.xdmf             # show some info about the mesh

meshioplusplus compress   input.vtu              # compress the mesh file
meshioplusplus decompress input.vtu              # decompress the mesh file

meshioplusplus binary     input.msh              # convert to binary format
meshioplusplus ascii      input.msh              # convert to ASCII format

meshioplusplus merge      a.vtu b.vtu out.vtu    # merge meshes (optional --weld)

meshioplusplus transform  in.vtu out.vtu --translate 1,2,3   # affine transform
meshioplusplus clean      in.vtu out.vtu --weld              # weld / prune / de-dup
meshioplusplus crop       in.vtu out.vtu --bbox 0,0,0,1,1,1  # subset by region
meshioplusplus split      in.vtu 'out_{key}.vtu' --by type   # partition
meshioplusplus stats      mesh.vtu                           # geometric statistics

meshioplusplus data info  mesh.vtu                           # summarize data arrays
meshioplusplus data calc  in.vtu out.vtu --point "s = norm(v)"   # derive a field
meshioplusplus data to-cell  in.vtu out.vtu --keys T         # point -> cell average
meshioplusplus data normalize in.vtu out.vtu --cell damage --to 0,1

with any of the supported formats.

The same verbs are available as a standalone C++ binary that needs no Python: grab a ready-to-run, statically-linked build for Linux/macOS/Windows from the GitHub Releases page, or build it yourself with build/configure.sh --cli --build (or -DMESHIOPLUSPLUS_BUILD_CLI=ON). It links only the C++ core, so point/cell sets (and convert -s/-d) — which live only in the Python Mesh — are unavailable there; use the Python CLI for those.

In Python, simply do

import meshioplusplus

mesh = meshioplusplus.read(
    filename,  # string, os.PathLike, or a buffer/open file
    # file_format="stl",  # optional if filename is a path; inferred from extension
    # see meshioplusplus convert --help 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 meshioplusplus

# 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 = meshioplusplus.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
meshioplusplus.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).

Skin extraction

meshioplusplus.extract_skin derives the boundary surface of a 3D volume mesh (the Kratos SkinDetectionProcess face-hashing algorithm — faces occurring exactly once are boundary; points are compacted, point_data follows):

vol = meshioplusplus.read("part.msh")     # tetra/hexa/wedge/pyramid mesh
skin = meshioplusplus.extract_skin(vol)   # triangle/quad/... surface mesh

The STL and PLY writers do this automatically for volume meshes (pass skin=False for the legacy drop-volume-cells behavior), and the SVG/TikZ writers render 3D meshes by projecting the skin through an orthographic camera (azimuth/elevation/roll in degrees, default the classic CAD isometric view) with painter's-algorithm depth ordering — that is exactly how the Stanford-bunny logo above is drawn.

Surface extraction

meshioplusplus.extract_surface is the general form of skin extraction: it picks the dimension automatically (a volume mesh → boundary faces, a 2D surface mesh → boundary edges) and can record each facet's parent cell id (record_parent_ids=True). See the surface extraction docs (doc/extract_surface.md).

surf = meshioplusplus.extract_surface(vol)                  # faces (or edges for a 2D mesh)
edges = meshioplusplus.extract_surface(sheet, record_parent_ids=True)

Mesh quality

meshioplusplus.compute_quality scores every cell on a set of geometric quality metrics (area/volume, scaled Jacobian, aspect ratio, skewness, interior/dihedral angles, warpage) and flags inverted/degenerate cells; attach_quality writes them back as cell_data. See doc/mesh_quality.md.

report = meshioplusplus.compute_quality(mesh)
print(report["num_inverted"], "inverted cells")
annotated = meshioplusplus.attach_quality(mesh)   # metrics as cell_data

Reordering / renumbering

meshioplusplus.reorder renumbers nodes and elements to reduce sparse-matrix bandwidth (Reverse Cuthill–McKee) or improve cache locality (Morton / Hilbert space-filling curves). It is a pure permutation — geometry and all data preserved — and returns the applied node/cell permutations so external arrays can be remapped. compute_bandwidth measures the before/after connectivity bandwidth. See doc/reorder.md.

out = meshioplusplus.reorder(mesh, method="rcm")            # "morton" / "hilbert" too
out, node_perm, cell_perms = meshioplusplus.reorder(mesh, return_permutation=True)
print(meshioplusplus.compute_bandwidth(mesh), "->", meshioplusplus.compute_bandwidth(out))

Comparison (diff)

meshioplusplus.diff compares two meshes and reports whether they are equivalent within a tolerance (abs_err <= atol + rtol*|expected|), with a structured breakdown (points, cells, data, named sets) and an overall verdict (identical / equal within tolerance / different); meshes_equal is the boolean wrapper for test suites. An optional unordered=True mode matches nodes by spatial proximity, so a shuffled node order still compares equal. See doc/diff.md.

assert meshioplusplus.meshes_equal(a, b, atol=1e-8)         # ideal in a regression test
report = meshioplusplus.diff(a, b, unordered=True)          # tolerant to shuffled node order
print(report["verdict"])

The meshioplusplus diff a.vtu b.vtu CLI verb sets a nonzero exit code when meshes differ, for direct use in CI / Makefiles.

Merge / combine

meshioplusplus.merge combines two or more meshes into one: it concatenates points (offsetting connectivity so indices stay valid), merges cell blocks by type, concatenates data (per a configurable data_policy), and tags each cell's origin. With weld=True it fuses coincident nodes across inputs within atol using a spatial hash (never O(N²)) — the standard way to stitch adjacent blocks into a watertight mesh. Overlapping set / field-data names are namespaced by source id. See doc/merge.md.

combined = meshioplusplus.merge([a, b, c])                 # concatenate
welded = meshioplusplus.merge([left, right], weld=True, atol=1e-8)  # fuse the shared interface

Editing (transform / clean / crop / split) and statistics

A bundle of dependency-free mesh-editing utilities:

  • meshioplusplus.transform — apply an affine transform (translate / scale / rotate / 4×4 matrix / unit-scale) to the points; connectivity and data are carried through. See doc/transform.md.
  • meshioplusplus.clean — weld coincident points (spatial hash), drop degenerate and duplicate cells, and remove orphaned points, in one toggleable pass. See doc/clean.md.
  • meshioplusplus.crop — extract the part of a mesh inside a bounding box or half-space, pruning unused points (mode="all"/"any"). See doc/crop.md.
  • meshioplusplus.split — partition a mesh into several by cell type, connected component (flood-fill), or region (cell_sets / integer tag). See doc/split.md.
  • meshioplusplus.compute_stats — geometric statistics (bounding box, centroid, per-type counts, area, signed/unsigned volume, inverted cells) — the geometric complement to info. See doc/stats.md.
out = meshioplusplus.transform(mesh, rotate=("z", 90))
out = meshioplusplus.clean(mesh, weld=True, atol=1e-8)
sub = meshioplusplus.crop(mesh, bbox=[0, 0, 0, 1, 1, 1])
pieces = meshioplusplus.split(mesh, by="type")             # {"triangle": ..., ...}
s = meshioplusplus.compute_stats(mesh)                     # dict of measures

These operations are exposed across every binding surface (Python, C API, Fortran, WASM) and as the CLI verbs meshioplusplus quality, meshioplusplus extract-surface, meshioplusplus reorder, meshioplusplus diff, meshioplusplus merge, meshioplusplus transform, meshioplusplus clean, meshioplusplus crop, meshioplusplus split, and meshioplusplus stats.

Data operations (rename / average / calc / condition / summarize)

A second bundle operates on the data arrays a mesh carries (point_data / cell_data / field_data) rather than on its geometry, which none of them ever modifies:

  • meshioplusplus.data_rename / data_drop / data_keep — rewrite which arrays a mesh carries and under what names; values, dtypes and shapes are copied verbatim. See doc/data_manage.md.
  • meshioplusplus.point_data_to_cell_data / cell_data_to_point_data — move data between locations by averaging, optionally weighted by cell area/volume. See doc/data_average.md.
  • meshioplusplus.data_calc — derive a new array from an elementwise expression (+ - * /, parentheses, abs/sqrt/min/max/norm) evaluated by a hand-written parser — no external parser library, no arbitrary-code path. See doc/data_calc.md.
  • meshioplusplus.data_condition — clamp, normalize to a target range, or standardize to zero mean / unit standard deviation, per component or by row magnitude. See doc/data_condition.md.
  • meshioplusplus.data_info — a read-only per-array summary (dtype, shape, components, min/max/mean, NaN/inf counts) — the data-side complement to info and compute_stats. See doc/data_info.md.
out = meshioplusplus.data_calc(mesh, "norm(velocity)", location="point", output="speed")
out = meshioplusplus.point_data_to_cell_data(out, keys=["speed"], suffix="_c")
out = meshioplusplus.data_condition(out, "cell", ["speed_c"], mode="normalize")
out = meshioplusplus.data_rename(out, "point", "T", "temperature")
arrays = meshioplusplus.data_info(out)                     # list of per-array dicts

These are likewise exposed across every binding surface, and as the nine CLI verbs under the meshioplusplus data group (info, rename, drop, keep, to-cell, to-point, calc, clamp, normalize). See doc/data_operations.md.

Time series

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

with meshioplusplus.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 meshioplusplus.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

gmsh paraview *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-meshioplusplus-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.

Benchmarks

How much does the C++ core help? The benchmark/ folder times read/write conversions against the original pure-Python meshio on the formats both support (same in-memory mesh, same machine). The headline input is the bundled example.msh — a real Gmsh bracket (~52k nodes, ~293k cells).

meshio vs meshio++ speedup on example.msh

meshio++'s biggest wins are the parallel and text paths: VTU binary+zlib ~16× write (the zlib blocks run across cores via an OpenMP backend with dynamic scheduling — hybrid P+E-core CPUs load-balance too), VTU ASCII ~7× write / ~5× read, and mixed-topology XDMF read ~10×. The binary and HDF5 formats that used to be slower — VTK/Gmsh binary, UGRID, and MED — are now at or above parity after an optimisation pass (bulk-buffered binary I/O, single-instruction bswap endianness conversion, a real parallel backend, an Eigen-backed MED transpose, zero-copy cell reconstruction that moves the connectivity buffer straight into the mesh, and uninitialised reader buffers + thread-parallel block copies so nothing is written twice); binary reads now match or beat numpy's fromfile — Gmsh ~1.7×, single-type VTK ~1.45×, and even mixed-topology VTK ~1.1×. Output stays byte-identical throughout.

The speedup is per-element: text/parallel formats climb out of the small-mesh regime and plateau (large meshes realise the full speedup):

speedup vs mesh size

Full methodology and a reproducible notebook are on the Benchmarks doc page (source: benchmark/01_benchmark.ipynb).

Reading only what you need

import meshioplusplus

mesh = meshioplusplus.read("big.vtu", points_only=True)   # geometry, no data arrays
mesh = meshioplusplus.read("big.vtu", arrays=["u", "p"])  # only these arrays
meta = meshioplusplus.read_metadata("big.vtu")            # counts/names, no heavy arrays

VTU, VTP, XDMF and Gmsh skip the unwanted array bodies outright; other formats are read in full and filtered, and meta["fell_back_to_full_read"] says which happened. Large files can also be memory-mapped (automatic above 16 MiB), which roughly halves peak memory during a read. See selective reads and memory-mapped reading.

VTK XML output can additionally use lz4 (ParaView-readable) or zstd (a meshio++ extension) instead of zlib, when built with -DMESHIOPLUSPLUS_WITH_LZ4=ON / -DMESHIOPLUSPLUS_WITH_ZSTD=ON. zlib remains the default. See compression codecs.

Installation

meshio++ is available from the Python Package Index, so simply run

pip install meshioplusplus

to install.

Additional dependencies (netcdf4, h5py) are required for some of the output formats and can be pulled in by

pip install meshioplusplus[all]

For JavaScript / browser use, the C++ core also ships as a WebAssembly npm package covering 29 of the formats above:

npm install @meshioplusplus/wasm

See the WebAssembly / JavaScript doc page for usage and the format-support table.

C / Fortran API

For HPC codes written in C or Fortran, the C++ core also builds as an installable shared library (libmeshioplusplus, pure-C99 header, pkg-config + find_package support) with a modern OO Fortran 2008 module on top:

./build/configure.sh --fortran --tests --build     # --c-api for the C API alone
cmake --install build/cpp-release --prefix /opt/meshioplusplus

mio_mesh* m = mio_read("in.msh", NULL);
printf("%lld points\n", (long long)mio_mesh_num_points(m));
mio_write("out.vtu", m, NULL);
mio_mesh_free(m);

use meshioplusplus
type(mio_mesh) :: m
call m%read("in.msh")
call m%write("out.vtu")
call m%free()

The C API is also packaged for Conan (root conanfile.py) and vcpkg (overlay port under ports/meshioplusplus/), both driving the same install/find_package path:

conan create . -o meshioplusplus/*:with_hdf5=True
vcpkg install meshioplusplus --overlay-ports=ports

Full mesh access (build meshes from raw arrays, zero-copy readback) is covered on the C API and Fortran doc pages.

Single-header C++

The whole C++ core is also amalgamated into one self-contained, STB-style header — single_include/meshioplusplus/meshioplusplus.hpp — with pugixml bundled and no external dependencies by default. Drop it in, no CMake or linking required:

// in exactly ONE .cpp:
#define MESHIOPLUSPLUS_IMPLEMENTATION
#include "meshioplusplus/meshioplusplus.hpp"
// elsewhere: just #include it (declarations only)

g++ -std=c++20 -I single_include main.cpp

It is generated by ./tools/amalgamate.sh and kept in sync by CI. See the single-header doc page (optional HDF5/netCDF/zlib formats via MESHIOPLUSPLUS_HAS_* macros).

C++ mesh backends

Standalone C++ builds (no Python) can swap the in-memory mesh structure at compile time via MESHIOPLUSPLUS_MESH_BACKEND — every format works identically under each backend:

  • MESHIO (default; the Python extension and PyPI wheels always use it) — mirrors the Python meshio.Mesh;
  • NATIVE — the fastest pure-C++ structure (canonical Float64/Int64 storage, cell-type enum, CSR ragged blocks); the WebAssembly build uses it;
  • KRATOS — a Kratos Multiphysics-style ModelPart (Nodes/Elements/Conditions/SubModelParts) plus a header-only templated bridge that populates a real Kratos::ModelPart with no Kratos build dependency.
./build/configure.sh --mesh-backend NATIVE --tests --build

See the C++ mesh backends doc page.

Testing

To run the meshio++ unit tests, check out this repository, install it with the test extras, and type

pytest tests/

License

meshio++ is published under the MIT license.

Project links

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Credits

Author: Vicente Mataix Ferrándiz

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License

MIT License (The MIT License (MIT))

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Python >=3.8

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Release history Release notifications | RSS feed

10.17.0

21 files

10.14.0

21 files

10.9.0

21 files

10.6.0

21 files

10.0.0

21 files

9.27.0

21 files

9.25.0

21 files

9.22.0

21 files

9.14.0

21 files

9.12.0

21 files

9.11.0

21 files

9.10.0

21 files

9.9.0

21 files

9.8.0

21 files

9.7.0

21 files

9.6.0

21 files

9.4.1

21 files

9.4.0

21 files

9.3.0

21 files

9.2.0

21 files

9.1.0

21 files

9.0.0

21 files

8.7.0

21 files

8.5.0

21 files

8.4.0

21 files

8.3.0

21 files

8.0.0

21 files

7.16.0

21 files

7.15.0

21 files

7.14.0

21 files

7.13.0

21 files

7.12.0

21 files

7.10.0

21 files

7.7.0

21 files

7.6.0

21 files

7.5.0

21 files

7.4.0

21 files

This release

7.3.0 This release

21 files

7.2.1

21 files

7.2.0

21 files

7.1.0

21 files

7.0.0

21 files

6.9.0

17 files

6.8.0

17 files

6.7.0

17 files

6.6.3

17 files

6.6.1

17 files

6.6.0

17 files

6.5.0

17 files

6.4.0

17 files

6.3.2

17 files

6.3.1

17 files

6.3.0

17 files

6.2.0

17 files

6.1.0

17 files

6.0.5

17 files

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