Skip to main content

Python library for reading and writing OVF (OOMMF Vector Field) files

Project description

PyPI PyPI - Python Version PyPI - Wheel PyPI - License Downloads pipeline status

pyOVF

A Python library for reading and writing OVF (OOMMF Vector Field) files used in micromagnetic simulations.

Features

  • Fast I/O: C++ backend for high-performance file operations (via ovf-rw)
  • NumPy Integration: Seamless conversion between OVF files and NumPy arrays
  • Pure Python Fallback: Works even without the C++ extension (slower but functional)
  • OOMMF & mumax3 Compatible: Supports files from both simulation packages
  • Binary Format: Reads and writes OVF 2.0 Binary 4 format
  • Wide Python Support: Python 3.8 - 3.14

Installation

pip install pyovf

Windows Users: Building from Source

⚠️ Git Bash / MINGW64 Users: If you're building from source on Windows using Git Bash, see WINDOWS_BUILD_SETUP.md for special instructions.

⚠️ Windows MAX_PATH Error: If pip install pyovf fails with a CMake error about path length exceeding 260 characters (MSB4018 / System.InvalidOperationException), see the path length fix below.

Quick Git Bash Install:

cd pyovf
chmod +x install_from_gitbash.sh
./install_from_gitbash.sh

Or use PowerShell:

cd pyovf
.\install_build_tools.ps1

For complete Windows setup instructions, see WINDOWS_BUILD_SETUP.md.

From Source (Linux/Mac)

git clone https://gitlab.flavio.be/flavio/pyovf.git
cd pyovf
pip install -e .

Building with ovf-rw

The C++ bindings are built from the ovf-rw library. When building from source, the build system will automatically fetch the required sources.

# Clone both repositories
git clone https://gitlab.flavio.be/flavio/pyovf.git
git clone https://gitlab.flavio.be/flavio/ovf-rw.git

# Build pyovf (it will find ovf-rw in the parent directory)
cd pyovf
pip install -e .

Quick Start

import pyovf
import numpy as np

# Read an OVF file
ovf = pyovf.read("magnetization.ovf")

# Or read with mesh objects (X and Y)
# X, Y, ovf = pyovf.read('magnetization.ovf', return_mesh=True)

print(f"Data shape: {ovf.data.shape}")
print(f"Grid: {ovf.xnodes}x{ovf.ynodes}x{ovf.znodes}")

# Access and modify data
mx = ovf.data[..., 0]  # X component
my = ovf.data[..., 1]  # Y component
mz = ovf.data[..., 2]  # Z component

# Create a new OVF file from scratch
data = np.zeros((1, 100, 100, 3), dtype=np.float32)
data[..., 2] = 1.0  # Uniform mz = 1

ovf_new = pyovf.create(
    data,
    xstepsize=5e-9,  # 5 nm cells
    ystepsize=5e-9,
    zstepsize=10e-9,
    title="m"
)

pyovf.write("uniform_state.ovf", ovf_new)

API Reference

Functions

pyovf.read(filename) -> OVFFile

Read an OVF file and return an OVFFile object.

pyovf.write(filename, ovf)

Write an OVFFile object to disk.

pyovf.create(data, **kwargs) -> OVFFile

Create a new OVFFile from a NumPy array.

OVFFile Properties

Property Type Description
data np.ndarray Field data (z, y, x, [dim])
xnodes, ynodes, znodes int Grid dimensions
xstepsize, ystepsize, zstepsize float Cell sizes
valuedim int Components (1=scalar, 3=vector)
Title str Data description
TotalSimTime float Simulation time

Data Layout

OVF files store data in column-major order:

  • For a vector field: data[z, y, x, component]
  • For a scalar field: data[z, y, x]

Supported Python Versions

Python Version Status
3.8 ✅ Supported
3.9 ✅ Supported
3.10 ✅ Supported
3.11 ✅ Supported
3.12 ✅ Supported
3.13 ✅ Supported
3.14 ✅ Supported (experimental)

Project Structure

pyovf/
├── pyovf/              # Main package
│   ├── __init__.py     # Package initialization
│   ├── _version.py     # Dynamic version (auto-generated by setuptools-scm)
│   ├── helper_funcs.py # Helper functions
│   └── ovf_handler.py  # OVF file handler + C++ backend loader
├── src/                # C++ pybind11 binding sources
├── tests/              # Unit tests
├── release.sh          # Single entry-point: build / test / tag / deploy
├── pyproject.toml      # Build configuration
├── setup.py            # CMake integration for C++ extension
└── CMakeLists.txt      # CMake build configuration

Related Projects

  • ovf-rw: The underlying C++ library for OVF file I/O, providing:
    • MATLAB bindings via MEX
    • Python bindings via Cython
    • High-performance binary file operations

Development

Setting up a development environment

git clone https://gitlab.flavio.be/flavio/pyovf.git
cd pyovf
python -m venv venv
source venv/bin/activate
pip install -e ".[dev]"

Running tests

pytest tests/ -v --cov=pyovf

Building wheels

./release.sh build              # builds wheel + sdist with current python3
./release.sh build --python 3.12  # build with a specific version

Versioning

This project uses setuptools-scm for dynamic versioning based on git tags. Version numbers are automatically determined from git history:

  • Tagged commits (e.g., v1.0.0) produce release versions (1.0.0)
  • Commits after a tag produce development versions (1.0.1.dev3+g1234567)

To create a new release use release.sh — it validates the working tree, creates an annotated tag, and pushes it to trigger the CI/CD pipeline:

./release.sh tag 0.3.0       # stable   → CI deploys to PyPI + GitLab Registry
./release.sh tag 0.3.0-rc1   # pre-release → CI deploys to Test PyPI + GitLab Registry

For a manual local deploy (bypass CI):

export PYPI_TOKEN="pypi-..."
./release.sh build && ./release.sh test && ./release.sh deploy --pypi

Troubleshooting

Windows MAX_PATH (260-character) Limit

Symptom: pip install pyovf fails with a CMake / MSBuild error like:

error MSB4018: System.InvalidOperationException: ... Le nom du fichier qualifié complet doit contenir moins de 260 caractères.

or in English:

The fully qualified file name must be less than 260 characters.

Cause: Windows limits file paths to 260 characters by default. The pip temporary build directory is already deep enough that the nested CMake scratch paths exceed this limit.

Fix 1 — Enable long paths (recommended, requires admin)

Run PowerShell as Administrator:

New-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem" `
    -Name "LongPathsEnabled" -Value 1 -PropertyType DWORD -Force

Restart your terminal, then pip install pyovf will work normally.

Fix 2 — Install from local source (no admin needed)

If you have the repository cloned, build from a short local path instead of pip's deep temp directory:

cd pyovf
pip install -e .

Fix 3 — Use a shorter TEMP directory (no admin needed, per-session)

set TEMP=C:\T
set TMP=C:\T
mkdir C:\T
pip install pyovf

For more Windows build troubleshooting, see WINDOWS_BUILD_SETUP.md.

License

MIT License - see LICENSE file for details.

Author

Prof. Flavio ABREU ARAUJO
Email: flavio.abreuaraujo@uclouvain.be

Citation

If you use this software in your research, please cite:

@software{pyovf,
  author = {Abreu Araujo, Flavio},
  title = {pyovf: Python library for OVF file I/O},
  year = {2021},
  url = {https://gitlab.flavio.be/flavio/pyovf}
}

Project details


Download files

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

Source Distribution

pyovf-0.2.14.tar.gz (96.1 kB view details)

Uploaded Source

Built Distributions

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

pyovf-0.2.14-cp314-cp314-manylinux_2_34_x86_64.whl (108.0 kB view details)

Uploaded CPython 3.14manylinux: glibc 2.34+ x86-64

pyovf-0.2.14-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (109.2 kB view details)

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

pyovf-0.2.14-cp314-cp314-macosx_14_0_arm64.whl (90.9 kB view details)

Uploaded CPython 3.14macOS 14.0+ ARM64

pyovf-0.2.14-cp313-cp313-win_amd64.whl (107.5 kB view details)

Uploaded CPython 3.13Windows x86-64

pyovf-0.2.14-cp313-cp313-manylinux_2_34_x86_64.whl (110.3 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.34+ x86-64

pyovf-0.2.14-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (109.3 kB view details)

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

pyovf-0.2.14-cp313-cp313-macosx_14_0_arm64.whl (90.6 kB view details)

Uploaded CPython 3.13macOS 14.0+ ARM64

pyovf-0.2.14-cp312-cp312-win_amd64.whl (13.8 kB view details)

Uploaded CPython 3.12Windows x86-64

pyovf-0.2.14-cp312-cp312-manylinux_2_34_x86_64.whl (110.3 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.34+ x86-64

pyovf-0.2.14-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (109.3 kB view details)

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

pyovf-0.2.14-cp312-cp312-macosx_14_0_arm64.whl (90.6 kB view details)

Uploaded CPython 3.12macOS 14.0+ ARM64

pyovf-0.2.14-cp311-cp311-win_amd64.whl (13.8 kB view details)

Uploaded CPython 3.11Windows x86-64

pyovf-0.2.14-cp311-cp311-manylinux_2_34_x86_64.whl (111.9 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.34+ x86-64

pyovf-0.2.14-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (109.4 kB view details)

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

pyovf-0.2.14-cp311-cp311-macosx_14_0_arm64.whl (91.1 kB view details)

Uploaded CPython 3.11macOS 14.0+ ARM64

pyovf-0.2.14-cp310-cp310-win_amd64.whl (13.8 kB view details)

Uploaded CPython 3.10Windows x86-64

pyovf-0.2.14-cp310-cp310-manylinux_2_34_x86_64.whl (110.1 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.34+ x86-64

pyovf-0.2.14-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (107.9 kB view details)

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

pyovf-0.2.14-cp310-cp310-macosx_14_0_arm64.whl (89.8 kB view details)

Uploaded CPython 3.10macOS 14.0+ ARM64

pyovf-0.2.14-cp39-cp39-win_amd64.whl (13.8 kB view details)

Uploaded CPython 3.9Windows x86-64

pyovf-0.2.14-cp39-cp39-manylinux_2_34_x86_64.whl (110.3 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.34+ x86-64

pyovf-0.2.14-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (108.2 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

pyovf-0.2.14-cp39-cp39-macosx_14_0_arm64.whl (89.9 kB view details)

Uploaded CPython 3.9macOS 14.0+ ARM64

File details

Details for the file pyovf-0.2.14.tar.gz.

File metadata

  • Download URL: pyovf-0.2.14.tar.gz
  • Upload date:
  • Size: 96.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for pyovf-0.2.14.tar.gz
Algorithm Hash digest
SHA256 d1654081f437b7f7110d6045422f16ecef8c9e3ba1d9db30874978f6333a393c
MD5 13f97bd6c9cb8da5180f9231cc326377
BLAKE2b-256 6b31fc0d65843412619c9ff1bc978666bbde7c4002a55673b7f9e6812453a9dc

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp314-cp314-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp314-cp314-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 d69a845ca88d2b3afe810abb88ef899298e6f176676207f2395db2079e4a9571
MD5 667ab93e8bd86cdf961a2b9d4721b955
BLAKE2b-256 79f84191185e1308434302a05ca7fea6e1630ee68d667aa51ba0612fef64edc7

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 fc5c76be9a182c980fd4575ac9c9b682fa343de5f9e26ad14560553ce1727f45
MD5 f3e68a5d10dfece7320dadf94cc18b16
BLAKE2b-256 04a5458a0d4cecd1ee785e2e8e3582d956faa3ac5b5c9f6da9a675c2eba453bf

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp314-cp314-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp314-cp314-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 832ed65c171d70f37a6767d8515fe87d571cf84432e856b59a22179f445e9304
MD5 6752332d64fa2358377771cb5a5292e9
BLAKE2b-256 1afa57d37124361a01950f06655a404386732ceb7f10d3541c36e040505f284c

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: pyovf-0.2.14-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 107.5 kB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.12

File hashes

Hashes for pyovf-0.2.14-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 10463a13aca76a5657d5787bafe01d6c531faf1bb12d80f1fd5ba33003460f01
MD5 dd874a3a654e3338b12ad4e0b8a32f6f
BLAKE2b-256 e069f11eacfa1afc82c60e87a39c4b8d1813e26477886a94d1473c904f71e415

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp313-cp313-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp313-cp313-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 4cab864f1b5ac5aaab96a978ac855b711e2a5b75a6e7163499a780cf467c05d2
MD5 c1ef9d3757a606f20ae7ffc64fd5f0bc
BLAKE2b-256 218f82c534bf50451e3019b789a2d06372576004dda14c0fe26feae4c763632a

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 5624c8a19ddb6a49366c2e87b1f7db275bd81c398cf7d228b740bc021b4f587c
MD5 d5a1ee7aee0bab958794b9d2cd28305a
BLAKE2b-256 74d057f585c7ae7b76969b80e994ed6bba0c7dec1013439e978327ad1c33720e

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp313-cp313-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp313-cp313-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 25af20f487f67ec7fb86ca1490bc8deb7ebb41643c3c6a95db68c093eb4de2be
MD5 9c9ad670a6ea2d30423380ed6c863b83
BLAKE2b-256 003079ebd6f8c996a11c77566f306d116b6ae059fdcc2d19ac3708ef5bfa4b08

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: pyovf-0.2.14-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 13.8 kB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.19

File hashes

Hashes for pyovf-0.2.14-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 3c97b705ae1c862ced6b2b5ee24b7c8f73e44d826e20cd605c319563b4886424
MD5 e6df9d94a363d94e254c43ca4a8b5b38
BLAKE2b-256 1b6c8f4a4e75932f8b345f960dd697b64f245a2e6e087a63326070cecdcfd353

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp312-cp312-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp312-cp312-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 7ff889b4cd95c6e15f5941175a4e2f2d84124e22fcc6edce7d240128a51e482e
MD5 fe3de476e561f846b299d4fa0d164409
BLAKE2b-256 6b7039f34b8f220b502a853fa0d47249c995245dd1a468b7d97d447b5752b8a0

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3c9df494e19f8ebda61cb849013b129fe8fe4e408bcd1abd958d1fe94fda5703
MD5 56dcd29ae189366589fb3ab6d01d959d
BLAKE2b-256 5092db59ca277296110e059e6773344425dc49b4e8287da3573be2299b6779f8

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp312-cp312-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp312-cp312-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 20c7d3e03a5cb6fab9bb24aa5bc10d58187fcb905c9729271e6298171f2d2bec
MD5 6a6d03c9b4a2f66cc3cd10625e6a4291
BLAKE2b-256 e8ffa20bb4477a3e006f4ea2c047840fa85021907d8cfbefd86eea90291d9d36

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: pyovf-0.2.14-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 13.8 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.19

File hashes

Hashes for pyovf-0.2.14-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 1bdd9ab418a0a84dbc2189b3c9fa45ca056029eefbcc7437cdc74da24ccd2dcf
MD5 15d8850812d8909bf16ab0de13e7b714
BLAKE2b-256 5bc706ca1552199e169281a87bae918084a53be5d05d0742e8acff30c437d47f

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp311-cp311-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp311-cp311-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 802118015df2db501839f6535f044acac0f77c8c09f82401b10e6b1b01b5938c
MD5 2ab91c4aa104f33dadf9916435d755df
BLAKE2b-256 5cded8fdcb528324176285ce59fc86c08f0044e25239a9d97ebd6b4faac46813

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f4f312a81ed70da2367f14c3abcdb5f08b5b10df913fc01cb527849e2c74efcd
MD5 857d888e0c8e4447bdcaeca320ac811b
BLAKE2b-256 cc8f59f0c1643a414f4c5686206d384ae58e499add5a81d5c14c9c1c5f36ef79

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp311-cp311-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp311-cp311-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 4b90d5ac57a57228b478927780214e2978ac8ea6010b2d07639998247e271800
MD5 cf43378baefd2590ff641242279c00fb
BLAKE2b-256 25c874d46ffbadeb61e593c9ac1b03f63e18ef5c2dd6c587e8d535d1b62994f0

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyovf-0.2.14-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 13.8 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.19

File hashes

Hashes for pyovf-0.2.14-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 d9ad82b8152a5f3e72f5848340fef1dda4381147341b3912abee5e31ddaf5edd
MD5 cfce5313ea30f4369ee1d73153f4c8fa
BLAKE2b-256 1b605cadcb55c72fed1615c4afa231b378839598b8513208c42767280e06c1b4

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp310-cp310-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp310-cp310-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 a8ce35d16b175cc84054ec82fdb11f30ba664d74145d7c1827a2837a13f020dd
MD5 f9f12d98ec3cc338d0998efee8ea6f34
BLAKE2b-256 1277cf0a355a6b253cfe7a23f47f93ff65a2e028f6bea800a54d0c321b376f05

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ec722aba7177babf1e0c8311df8abf691a9ffa03fe647ec5ec16522fcdd9f3c9
MD5 0413958088409c408e76959a5a7ef865
BLAKE2b-256 3015bac0a7a109d0fe2bbf6dd8334efa2b1c1a4c504a0543f690a14f1607a664

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp310-cp310-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp310-cp310-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 0f83aa36d8a32d7af621f62cee4789f46285f42f3632b6bd25dabf552f03c1c4
MD5 b29bdee47942ea8b4ddd41a7fafd7cfa
BLAKE2b-256 83cd0c1e6e460a7f84fb95c4f2c9382d94c04a66ce426abc9fe30a270e215e8c

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyovf-0.2.14-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 13.8 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.19

File hashes

Hashes for pyovf-0.2.14-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 ed663ca9e5e467f39b6d471e9c1e8ef2bb3d78981f164c2a5021b821f025e65b
MD5 6bae3db13a362385aa9f95fd10d72358
BLAKE2b-256 d51f052acc7ae597134889bcfc59c73d1707ff5b6ae11771d304cf1edf08b96c

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp39-cp39-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp39-cp39-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 562d54f86691da2c94a985279e05c52d3a53fefcf2240cc083c59782508caae5
MD5 34ca4ddcaec634800ad2a0c9feb0cb41
BLAKE2b-256 72bdf8e729c88abb128f739fc416440651142a4c3e07e7a7f9b13906b5ec2411

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a8927898a9867989fadcbfdf4d6a396d6dc445a212b1381142530dc7394981da
MD5 ef3478aebe3fd76254182fdf604d3e2f
BLAKE2b-256 be14f28cc8e26f19667b45636593986bbac98575bee598937ccd5fba3c5ccf86

See more details on using hashes here.

File details

Details for the file pyovf-0.2.14-cp39-cp39-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pyovf-0.2.14-cp39-cp39-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 3a01ed02ae5f1530a1b9ec1681c94e36aa6aee04f1689a38f85cda0dd040296b
MD5 a695c77917e4ea353227f65864c266ff
BLAKE2b-256 921e740f757827d118d779f863f3eec2c552f5b4b4983a6adbbfc9f96e0173ca

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page