Skip to main content

mmgpy

PyPI conda-forge Python License Docs codecov

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.

Gallery

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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mmgpy-0.17.0.tar.gz (19.6 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

mmgpy-0.17.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.8 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.17.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.5 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.17.0-cp314-cp314-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.14Windows x86-64

mmgpy-0.17.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.17.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.5 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.17.0-cp314-cp314-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

mmgpy-0.17.0-cp313-cp313-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.13Windows x86-64

mmgpy-0.17.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.17.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.5 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.17.0-cp313-cp313-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

mmgpy-0.17.0-cp312-cp312-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.12Windows x86-64

mmgpy-0.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.17.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.5 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.17.0-cp312-cp312-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

mmgpy-0.17.0-cp311-cp311-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.11Windows x86-64

mmgpy-0.17.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.17.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.5 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.17.0-cp311-cp311-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

mmgpy-0.17.0-cp310-cp310-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.10Windows x86-64

mmgpy-0.17.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.17.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.5 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.17.0-cp310-cp310-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

File details

Details for the file mmgpy-0.17.0.tar.gz.

File metadata

  • Download URL: mmgpy-0.17.0.tar.gz
  • Upload date:
  • Size: 19.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mmgpy-0.17.0.tar.gz
Algorithm Hash digest
SHA256 96f97a66c98243cc4e29641f6e22672a014114adbef7c73f9a878f0887589761
MD5 234eaadf57711932f88957d081ae793f
BLAKE2b-256 571de9dedf706e59c78bbde592b169bef708c2b9501231f2baa6ef110c970871

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 d7da820791ed42bcdbca0dd981d9db5895f59972193a7013c99b96d5634b15a2
MD5 0339e46d7e4cd97067c0a37843f58600
BLAKE2b-256 81a86f8b86e75dfe1bb70901fb1da80af04fa8e3e2ba0810e7285ca2277f6261

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 8f430516b7bf6df62961926a9df900badc618310416ba503561a8c695ee3a574
MD5 f6b617107ba1e90c8a230e55688e5c24
BLAKE2b-256 a808808051d65ff46e181e5bf1ac683692a2f973368ec191333128b67d9c2a87

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.17.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mmgpy-0.17.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 20929f4c826bec8d53f19122776536b15451ebe436e2f50ce1ec65f1bc099ca5
MD5 f6fcac14d93c03c94b74db2927d11fef
BLAKE2b-256 b93fd29c0da61819535f129de206d5ea2190133b106309485c9bf1e13efe0791

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ec589792a1989c8259a203e2bf86caa96bcda09fd86bb797b727d300db30dc4b
MD5 62c30be284a57203e311295b18f0e930
BLAKE2b-256 4d9d43e919cf1ba09c5667fb838565c91b20229636e01356f5964be13c4f046b

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 37d5ed67df3929c088bee66c226153b9be9080d7016891ede74f15135fc2197f
MD5 a53b841a1b646d54c75c44e29e08e939
BLAKE2b-256 878f5bb3ccb1593b5b3423e395d1b3c60e6ab8bd298ff0ee7ebc93d50a95fd77

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 970ffa150ee874c1e46f2efc2faae4f6d47c6b895d26f968ba15b8d3353af0b9
MD5 77a3d6e7f1a26319b3df4f03c659cd81
BLAKE2b-256 7ce7b6a10395234af37df10982477c486311ee1c01320102fb0ef0c2ff180760

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.17.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mmgpy-0.17.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 264b7b1b5789edc97124240ddb546da012bf03b9f30d97f78a2f47b116d46e60
MD5 ee2ad8ee2acb039e4e95b42ac8ab81ff
BLAKE2b-256 74381ecc277e7add92711fd475e5287c8486fad43e9b2ddda6bf5a47de4bb66f

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c9dabcb9c05653c812d9a73237f611b166362123bcb57d7a3a2bf21d58a85d40
MD5 771b5101fd664591d2c5ea4c78a3443d
BLAKE2b-256 b098e92bdb4b581a712f54f7cdbec52a33d1be1227706ee7f63263e6468c0147

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 83b22c626a18d039d15324620e0fdce6b6852884bd49fa680abb1e59c76ebfe1
MD5 151220adba6e92398db2ed3606a90d29
BLAKE2b-256 228b8d892f0deb3d87dc7fa19efeb75ca818699d20919d488ef2cc639c677c0a

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a0ff4e3f1509c6e0fa3b5040c90f397f591741f4103954141f69fcfc999dede6
MD5 8ca76efa19bfe3ff7941c1a54d34ccb3
BLAKE2b-256 56ac30841a907099153d60aaeab933c4026ceb51758d06c8988b6c622ae10e53

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.17.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mmgpy-0.17.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 4dbd477f6dd3c0910cf091d6bf20a199206bbcdf293c56794b303e3a46c4a068
MD5 9baedcf73bc92c5691adc61762c87790
BLAKE2b-256 3eea9a437e2036a420a88244dc2dcf86b3a59027524a641ba2cb36a25f506261

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0398dd92b486844b0a4fa3837619118e0cb292df74214d0b5a39c1e2a4f27f80
MD5 c2c48aa0464a932c4fb1661595427d5c
BLAKE2b-256 7f85979c1e1332774bcf9952efb95f29a542e44da87a89f7a2f4062b5978fbca

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 41472f08e8420eb4aedb55034dc7d6b42ca9a49e56b00c451b732b49cb05d038
MD5 bcdcb451ab9dd6c92ece9bebdbbfc8ac
BLAKE2b-256 f19a990ad9713771216c465f4929037eb03170482f9198896094f5926a461c66

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 acf9f132f4d111976f5de30d44f9f6467c906771e0ace0e8932e2956031d9a9b
MD5 d2aa767dfc71ff6df23b29740275d460
BLAKE2b-256 aec2bffbac55aa0b426efcfc010887d1c3bde5686f9436dfa76d83c7688934e3

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.17.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mmgpy-0.17.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 69a8e380abe520960793c71407d7395b9489e3e0a57a0c2bb3605705c4970e93
MD5 8a7335fd955c091e5d2d4b84e17c0342
BLAKE2b-256 4be4fef7d8714efb7bda7523fd86370c0944789712f1a81cbfa5011ef5c48a0a

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 fd4a93a6a3a48399d5189368bf5a8cd2b3943ad5168f5b0b31674ced2e391405
MD5 04b3fb4581fc7e29c86fa998fe707fdc
BLAKE2b-256 a7232a6c690e155ade89699ecc850605b68138adca842d0d14dda741f71f5e7f

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 d3990607727279af6d2e1e0c79b5793b2b46635750cd8d2b8c9d46ceeeb3d182
MD5 6c9cb90242219d2ff0cbca8ee348ae7c
BLAKE2b-256 94668ed58320a95f62dc016f8186331e0f4276f7f4b39a65e561058c77aecf76

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 253f9af457fc60771ba5af0d4ac11aa2d1e682e0240c0e4a9a9842b9f31912fe
MD5 267ba1fc9f3d790decbae21286b453c3
BLAKE2b-256 2d7986b0dce0bc211907b8b59a9977b5ca07f852a6bce289846f17c1fa8cae0f

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.17.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mmgpy-0.17.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 85787757bd8aaccfcf41df923884686c0911b8f80f9bf76bff91b4c4b03e8cd0
MD5 3660f03a45ff284d75e0ae90973ba85b
BLAKE2b-256 7adb4574fa6a42b4c0de14a8758a90fa68e00dbe18e6e6242d3c60912e956863

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9cdd976d5ba2b48465c948e4546b19e8b0b40d2f1fbe4952de0f6d8589a5a6ee
MD5 05b898e8aaff14e77884e757e807f502
BLAKE2b-256 028b0e306b4379034d963ebf7bf7c32a7bc10c2f6285a8115467234c2f10966d

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 53aa06e0c7dcdaa1d1cdbe96111c97d736550b4a498b1e694ebeca0c3d31afa7
MD5 e4547b7a564998cfaf0edcaf53ea8c65
BLAKE2b-256 31b51dcc4f93e62be80b4e2feb5333eca60e6a5642c7ebe203ffa6ebfa9b4754

See more details on using hashes here.

File details

Details for the file mmgpy-0.17.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.17.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bf098d43c87e906dfbbac08c15b19d8a5df5aa923d92d112c6daacd4c1d03d95
MD5 1f0120f9d9a20d42df145f2b8878ec83
BLAKE2b-256 ac9abd53d63ddf4435d5aab9319aad754433c922eef0009b7dee1b4a440aee54

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

0.17.0 This release

23 files

0.16.2

23 files

0.16.1

23 files

0.15.0

23 files

0.14.0

23 files

0.13.0

23 files

0.12.0

23 files

0.11.0

23 files

0.10.0

23 files

0.9.0

23 files

0.8.0

19 files

0.7.1

17 files

0.6.0

17 files

0.5.2

17 files

0.4.0

17 files

0.1.5

16 files

0.1.0

9 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page