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mmgpy

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mmgpy brings the power of MMG mesh adaptation to Python. Generate, optimize, and refine 2D, 3D, and surface meshes through a native PyVista accessor.

import pyvista as pv
import mmgpy  # noqa: F401  -- registers the .mmg accessor + Medit reader/writer

mesh = pv.read("input.mesh")
remeshed = mesh.mmg.remesh(hmax=0.1)
remeshed.save("output.vtk")

Mechanical piece remeshing

Try It

No installation needed, run directly with uvx:

# Remesh a mesh file from the command line
uvx mmgpy input.stl -o output.mesh -hmax 0.1

# Launch the interactive UI
uvx --from "mmgpy[ui]" mmgpy-ui

Installation

The recommended way to install mmgpy:

uv pip install mmgpy

This uses pre-built wheels from PyPI that bundle all native libraries (MMG, VTK), no compiler needed.

Other install methods

# pip
pip install mmgpy

# conda-forge
conda install -c conda-forge mmgpy

# With UI support
uv pip install "mmgpy[ui]"

# With elasticity-based displacement propagation
uv pip install "mmgpy[fem]"

PyVista accessor (mesh.mmg.remesh(...))

The .mmg accessor and the Medit .mesh / .meshb reader/writer (see How it works) activate whenever pyvista >= 0.48 is importable in the same environment as mmgpy. Either of these gives you that:

pip install mmgpy pyvista        # mmgpy + latest pyvista
pip install "mmgpy[pyvista]"     # same effect, version-pinned to >=0.48,<1

If pyvista isn't installed (or is older than 0.48, which predates the plugin entry-point system), the accessor simply isn't registered. import mmgpy still works, and the in-memory MmgMesh2D / MmgMesh3D / MmgMeshS API and mmgpy.mmgs.remesh(...) file path stay available.

Without PyVista

For headless / server / CI use, the slim install (pip install mmgpy, no pyvista, no VTK) exposes the C-binding mesh classes directly. Build a mesh from numpy arrays, attach fields via item syntax, and remesh in place:

import numpy as np
from mmgpy import MmgMeshS

mesh = MmgMeshS(vertices, triangles)        # numpy arrays, shape (Nv, 3) and (Nt, 3)
mesh["metric"] = sizing_array               # optional: per-vertex isotropic size
mesh.remesh(hmin=0.01, hmax=0.1, hausd=0.005)

verts_out = mesh.get_vertices()
tris_out = mesh.get_triangles()

MmgMesh2D (planar triangular) and MmgMesh3D (tetrahedral) follow the same pattern. File-based round trips are also available without pyvista via mmgpy.mmg2d.remesh(in_path, out_path, options={...}) and its mmg3d / mmgs siblings.

Using uv for project management

uv add mmgpy                 # add to project dependencies
uv tool install mmgpy        # install CLI tools globally
uv tool install "mmgpy[ui]"  # install CLI tools + UI globally

PyPI vs conda-forge

PyPI (pip/uv) conda-forge (Linux/macOS)
Install speed Fast (pre-built wheels) Slower (solver + download)
Dependencies Bundled (self-contained) Shared across packages
Disk usage Larger (duplicate VTK/libs) Smaller in conda environments
Best for Quick setup, isolated use, CI Scientific stacks sharing VTK/NumPy

Use PyPI (uv pip install) for the fastest setup. Use conda-forge when you already have a conda environment with VTK, PyVista, or other scientific packages.

How it works

Importing mmgpy registers a PyVista plugin that adds two things to every pv.UnstructuredGrid and pv.PolyData:

  • A .mmg accessor that exposes the full MMG API: remesh, remesh_optimize, remesh_uniform, remesh_levelset, move, validate, element_qualities, and more.
  • A Medit reader/writer for .mesh and .meshb files (with auto-loading of companion .sol files into point_data / cell_data).

Every accessor call returns a fresh PyVista dataset, so the result composes with the rest of the PyVista API (slicing, plotting, IO).

Features

  • Multi-dimensional, 2D triangular, 3D tetrahedral, and surface meshes (auto-detected from cell types via dataset.mmg.kind).
  • Local refinement, sphere / box / cylinder / point-based sizing, passed as local_sizing=[...] on remesh.
  • Anisotropic adaptation, metric tensors in point_data["metric"], including least-squares Hessian recovery from a scalar field.
  • Level-set discretization, extract isosurfaces from implicit functions via mesh.mmg.remesh_levelset(...); multi-material splits via set_multi_materials.
  • Lagrangian motion, move boundaries and remesh through mesh.mmg.move(displacement, ...), with a Laplacian propagator or an optional elasticity backend (fedoo).
  • Required entities, lock vertices, edges, triangles, or tetrahedra during remeshing via kwargs (required_triangles=...) or mmg_* data tags.
  • Companion .sol I/O, scalar / vector / tensor fields via load_sol, save_sol, load_all_sols, save_all_sols.
  • Validation & quality, mesh.mmg.validate(detailed=True) returns a ValidationReport; mesh.mmg.element_qualities() returns MMG's in-radius ratios.
  • 40+ file formats, native Medit, plus everything PyVista supports (VTK, STL, OBJ, GMSH, MED, Abaqus, etc.; install pyvista[io] for meshio-backed formats).

Usage

Basic remeshing

import pyvista as pv
import mmgpy  # noqa: F401

mesh = pv.read("input.mesh")
remeshed = mesh.mmg.remesh(hmax=0.1)

q_before = mesh.mmg.element_qualities()
q_after = remeshed.mmg.element_qualities()
print(f"Quality: {q_before.mean():.2f} -> {q_after.mean():.2f}")

remeshed.save("output.vtk")

Local sizing

Refine inside specific regions without touching the rest of the mesh:

remeshed = mesh.mmg.remesh(
    hmax=0.1,
    local_sizing=[
        {"shape": "sphere", "center": [0.5, 0.5, 0.5], "radius": 0.2, "size": 0.01},
        {"shape": "box", "bounds": [[0, 0, 0], [0.3, 0.3, 0.3]], "size": 0.02},
        {"shape": "cylinder", "point1": [0, 0, 0], "point2": [0, 0, 1],
         "radius": 0.1, "size": 0.01},
        {"shape": "from_point", "point": [0.5, 0.5, 0.5],
         "near_size": 0.01, "far_size": 0.1, "influence_radius": 0.3},
    ],
)

Typed options

from mmgpy import Mmg3DOptions

opts = Mmg3DOptions(hmin=0.01, hmax=0.1, hausd=0.001)
remeshed = mesh.mmg.remesh(opts)

# Or use presets
remeshed = mesh.mmg.remesh(Mmg3DOptions.fine(hmax=0.05))

Anisotropic metrics

Drop a per-vertex metric on point_data["metric"] and remesh() picks it up:

import numpy as np
import mmgpy.metrics as metrics

sizes = np.full(mesh.n_points, 0.05)
mesh.point_data["metric"] = metrics.create_isotropic_metric(sizes)

remeshed = mesh.mmg.remesh()

For solution-adaptive remeshing, recover a Hessian and convert it to a metric:

from mmgpy.metrics import compute_hessian, create_metric_from_hessian

hessian = compute_hessian(vertices, triangles, field)
mesh.point_data["metric"] = create_metric_from_hessian(
    hessian, target_error=5e-3, hmin=3e-3, hmax=8e-2,
)
remeshed = mesh.mmg.remesh(hgrad=2.0)

Level-set discretization

import numpy as np

levelset = (
    np.linalg.norm(mesh.points - [0.5, 0.5, 0.5], axis=1) - 0.3
).reshape(-1, 1)

discretized = mesh.mmg.remesh_levelset(levelset)

Lagrangian motion

Apply a per-vertex displacement and remesh to maintain element quality:

import numpy as np

displacement = np.zeros((mesh.n_points, 3))
displacement[:, 0] = 0.1

moved = mesh.mmg.move(displacement, hmax=0.1)

Pass only boundary values plus propagate=True to fill the interior. The default is a Laplacian smoother; pass propagation_method="elasticity" to use the fedoo-backed linear-elasticity solver (uv pip install "mmgpy[fem]").

Locking entities

Keep specific vertices, edges, triangles, or tetrahedra fixed during remeshing:

remeshed = mesh.mmg.remesh(
    hmax=0.1,
    required_triangles=np.array([3, 7, 11], dtype=np.int32),
)

Or attach the constraint to the dataset (it travels through save / copy):

mask = np.zeros(mesh.n_cells, dtype=bool)
mask[[3, 7, 11]] = True
mesh.cell_data["mmg_required_triangles"] = mask
remeshed = mesh.mmg.remesh(hmax=0.1)

Visualization

remeshed.plot(show_edges=True)

The accessor returns a regular PyVista dataset, so anything PyVista does (slicing, integration, custom plotters) works directly on the result.

Command Line

MMG executables are bundled with the wheel:

# Auto-detect mesh type
mmg input.mesh -o output.mesh -hmax 0.1

# Or use specific commands
mmg3d input.mesh -o output.mesh -hmax 0.1
mmgs surface.stl -o refined.mesh -hausd 0.001
mmg2d domain.mesh -o refined.mesh -hmax 0.05

# Check versions
mmg --version

The _O3 suffix variants (mmg3d_O3, etc.) are also available for compatibility.

Surface remeshing

Smooth surface optimization

3D quality improvement

Documentation

kmarchais.github.io/mmgpy

Contributing

Contributions are welcome. See CONTRIBUTING.md for development setup, coding standards, and the pull request process.

License

MIT

Metadata

Release files for mmgpy 0.17.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for mmgpy 0.17.1
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mmgpy-0.17.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.14 CPython 3.14 free-threading Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
mmgpy-0.17.1-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl CPython 3.14 CPython 3.14 free-threading Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 Details
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mmgpy-0.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.14 CPython 3.14 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
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mmgpy-0.17.1-cp314-cp314-macosx_11_0_arm64.whl CPython 3.14 CPython 3.14 macOS 11.0+ ARM64 Details
mmgpy-0.17.1-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
mmgpy-0.17.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
mmgpy-0.17.1-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 Details
mmgpy-0.17.1-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
mmgpy-0.17.1-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
mmgpy-0.17.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
mmgpy-0.17.1-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 Details
mmgpy-0.17.1-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
mmgpy-0.17.1-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
mmgpy-0.17.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
mmgpy-0.17.1-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 Details
mmgpy-0.17.1-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
mmgpy-0.17.1-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
mmgpy-0.17.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
mmgpy-0.17.1-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl CPython 3.10 CPython 3.10 Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64 Details
mmgpy-0.17.1-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details

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Release history Release notifications | RSS feed

This release

0.17.1 This release

23 release files

0.8.0

19 release files

0.7.1

17 release files

0.5.2

17 release files

0.4.0

17 release files

0.1.5

16 release files

0.1.0

9 release files

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