openptv2

Particle Tracking Velocimetry, single Cython 3 pure-Python engine

Python License Documentation Coverage Code health

Overview

openptv2 is a single-engine PTV library: the same src/openptv2/algorithms/ modules run interpreted in development and compiled through Cython 3 when built, with no separate C core, no Cython bindings to an external library, and no runtime engine selector.

  • Algorithms (src/openptv2/algorithms/) - Cython 3 pure-Python particle tracking, correspondence, and calibration code
  • Plugins (src/openptv2/plugins/) - pluggable tracker implementations (fast, MyPTV/ProPTV-informed trackers)
  • GUI (src/openptv2/gui/) - TraitsUI/Chaco desktop application
  • Batch pipeline (openptv2-batch) - headless sequence/tracking runner for scripting and cloud use

What's Inside (and Where the Tracking Concepts Come From)

openptv2 is a self-contained PTV engine: detection, camera calibration, correspondence/stereo-matching, and the classic forward tracking loop are all implemented from scratch here in src/openptv2/algorithms/.

On top of that native engine, openptv2 ships plugins that bring in tracking concepts from two external open-source projects:

  • MyPTV — MIT licensed (© 2022 Ron Shnapp). openptv2's myptv_2d_tracking / myptv_3d_tracking plugins adapt MyPTV's algorithm ideas (2D per-camera image-space tracking with multi-camera consensus, and 3D kinematic prediction + linear-assignment matching) onto openptv2's own data structures and assignment machinery.

  • proPTV — MIT licensed (© 2023 DLR, Robin Barta). openptv2 vendors the small pure-numpy core of proPTV (src/openptv2/plugins/proptv/): the Gaussian-Mixture-Model / basis approximation and Savitzky-Golay smoothing routines used in proPTV's track prediction. The proptv_tracking plugin wires those routines into openptv2's tracker.

Important: openptv2 incorporates and adapts parts of these projects as plugins — not their full frameworks. The complete tracking capabilities of each project — e.g. proPTV's 2D-image triangulation pipeline, Soloff calibration, backtracking/repair and Eulerian field estimation, or MyPTV's full photogrammetry, trajectory smoothing and anisotropic-particle tools — live in and are available only from their own repositories:

If you need those advanced features, please use those projects directly. Both are permissively MIT-licensed (see License).

Key Features

  • Single runtime: one codebase, no C/Cython vs. Python fallback split to keep in sync
  • Pluggable trackers: swap tracking algorithms via the plugin architecture
  • Easy installation: pip install openptv2 for headless/batch use, openptv2[gui] to add the desktop GUI

Installation

There are three install profiles:

Command For Includes
pip install openptv2 scripting + openptv2-batch (default) headless algorithms/API + sequence/tracking pipeline
pip install openptv2[gui] desktop users default plus the TraitsUI/Chaco/PySide6 desktop GUI
pip install openptv2[dev] contributors everything: GUI, tests, lint, type-check, notebooks, docs

Default (headless / batch)

The bare install is the headless batch runtime — the library, the openptv2-batch sequence + tracking pipeline, no GUI:

uv pip install openptv2
# or
pip install openptv2

Don't have uv? curl -LsSf https://astral.sh/uv/install.sh | sh

GUI (desktop users)

uv pip install openptv2[gui]
# or
pip install openptv2[gui]

Verify Installation

python -c "import openptv2; print(f'openptv2 version: {openptv2.__version__}')"
python -c "from openptv2 import Tracker; print('Tracker imported successfully')"

Note: launching the GUI (openptv2-gui) requires the [gui] extra. A default install that then launches the GUI will fail with ModuleNotFoundError: No module named 'traitsui' (and chaco/PySide6) — that is expected; install openptv2[gui].

Docker (no Python setup required)

A single image serves both the GUI and batch. It bakes in a trimmed test_cavity demo at /demo/test_cavity so the first run works with no data.

# Build once
docker build -t openptv2 -f docker/Dockerfile .

# GUI on the host X display (Linux/X11), current folder mounted at /data
./docker/run-gui.sh
#   in the GUI, open /demo/test_cavity to try the baked demo

# Headless batch on your own data
docker run --rm -v "$PWD:/data" openptv2 \
  openptv2-batch /data/<experiment>/parameters_Run1.yaml <first> <last>

X11 notes: run-gui.sh handles xhost and mounts /tmp/.X11-unix. On Wayland run xhost +local:root in an XWayland session; on macOS/Windows use an X server (XQuartz / VcXsrv) and set DISPLAY accordingly.

Headless cloud batch: docker/Dockerfile.cloud is a slim, no-GUI, free-threaded 3.14t image for servers/Cloud Run. See docs/cloud-batch.md for the one-command install, openptv2-batch usage, and measured timings.

Zarr + HDF5 Cloud Storage: OpenPTV2 includes a native, high-performance Zarr storage engine (res/run.zarr) replacing thousands of per-frame text files with a cloud-native chunked format. See docs/zarr-hdf5-storage.md for usage, terminal inspection, and Flowtracks HDF5 export.

For Developers (Build from Source)

Prerequisites:

  • Python 3.11–3.14 (free-threaded 3.14t works too, see docker/Dockerfile.cloud)
  • C compiler (gcc on Linux, clang on macOS, MSVC Build Tools on Windows) — needed to build the Cython 3 extensions, no CMake involved
  • uv (recommended) or pip

System Dependencies

Linux (Debian/Ubuntu):

sudo apt-get update
sudo apt-get install -y build-essential python3-dev

Linux (Fedora/RHEL):

sudo dnf install -y gcc gcc-c++ python3-devel

macOS:

xcode-select --install

Windows:

Step 1: Clone the Repository

git clone https://github.com/alexlib/openptv2.git
cd openptv2

Step 2: Install Dependencies and Build

Using uv (recommended):

# Sync all dependencies and build the package
uv sync --extra dev

Using pip:

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install build dependencies
pip install setuptools cython numpy

# Install in development mode
pip install -e ".[dev]"

The default editable install builds the Cython 3 pure-Python modules:

uv pip install -e .

Step 3: Verify Build

Always run verification inside your virtual environment to ensure the compiled libraries are correctly found:

# Test imports
uv run python -c "import openptv2; print(f'openptv2 version: {openptv2.__version__}')"
uv run python -c "from openptv2 import Tracker; print('Tracker imported successfully')"

# Run core tests
uv run pytest tests/unit/ -v

GUI Dependencies

The [gui] extra includes:

  • traitsui, enable, chaco (Enthought framework + visualization)
  • PySide6 (Qt bindings)
  • matplotlib, pandas, flowtracks (analysis)

(scikit-image and numpy are core dependencies, installed either way.)

Installing from Binary Wheels

Pre-built wheels are published to PyPI:

# Using pip
pip install openptv2

# Using uv
uv pip install openptv2

Troubleshooting Installation

Common Issues

1. "C compiler not found"

# Linux: sudo apt-get install build-essential
# macOS: xcode-select --install
# Windows: Install MSVC Build Tools

2. "Cython not found"

pip install cython>=3.0.0

3. "NumPy version mismatch"

pip install numpy>=2.0.0

4. Cython extensions not rebuilt after editing src/openptv2/algorithms/

uv run python setup.py build_ext --inplace

Usage

Basic Tracking

The quickest way to run a full detection → correspondence → tracking pipeline is the batch CLI (see Batch Processing below) or the GUI. For scripting against the library directly — loading calibrations/parameters and driving openptv2.Tracker — see docs/tutorials/.

Runtime

import openptv2

print(openptv2.get_runtime_info())
# {'engine': 'cython3-pure-python', 'compiled': True/False, 'package': 'openptv2'}

GUI

Launch the graphical interface using the unified console scripts. Ensure your virtual environment is activated (source .venv/bin/activate) or prefix the commands with uv run:

# Launch the GUI using the standard openptv2-gui or shorter pyptv_gui shortcut
uv run pyptv_gui

# Launch the GUI in the single-engine runtime
uv run pyptv_gui --workdir=./test_data/test_cavity

Batch Processing

Run high-throughput processing sequences using command-line batch utilities (ensure your virtual environment is activated or use uv run):

# Run batch processing with the single runtime
uv run pyptv_batch --workdir=./test_data/test_cavity --first=10000 --last=10005

# Run with legacy positional arguments (for backward compatibility)
uv run pyptv_batch ./test_data/test_cavity/parameters_Run1.yaml 10000 10004

# Parallel batch processing (distributes frame chunks to multiple cores)
uv run python -m openptv2.batch.pyptv_batch_parallel test_data/test_cavity/parameters_Run1.yaml 10000 10004 4 --mode sequence

### Parallel Processing Config
OpenPTV2 supports multi-core parallel processing during image preprocessing and target detection (Approach C). This is highly effective for accelerating execution on multi-core systems.

* **GUI Configuration**: Open the **Main Parameters** dialog, navigate to the **Sequence** tab, check the **Parallel Pre-processing** box, and set the **Number of workers** (e.g., `4` or `0` for automatic core detection).
* **CLI/Environment Configuration**: Set environment variables before running any tracking or batch sequence:
  ```bash
  export OPENPTV_PARALLEL_PREPROCESS=True
  export OPENPTV_NUM_WORKERS=4  # Set worker processes (omitting uses all CPU cores)

Command-line Shortcuts and Running Without uv

You can run these command-line tools without prefixing them with uv run using any of the following approaches:

1. Activate the Virtual Environment (Standard Workflow)

By activating the project's virtual environment, the environment's bin/ directory is added to your shell's PATH. This registers all entry points (like pyptv_gui, pyptv, pyptv_batch) directly in your terminal:

source .venv/bin/activate

# Now run directly without uv
pyptv_gui -w ./test_data/test_cavity

2. Execute via Direct Path

If you do not wish to activate the virtual environment, you can run the built executable directly from the local .venv folder:

./.venv/bin/pyptv_gui -w ./test_data/test_cavity

3. Define Shell Aliases (Global Access)

To run these shortcuts cleanly from any directory without manual paths, add alias entries to your shell profile (e.g., ~/.bashrc or ~/.zshrc):

# Add these lines to ~/.bashrc or ~/.zshrc
alias pyptv_gui='/home/user/Documents/GitHub/openptv2/.venv/bin/pyptv_gui'
alias pyptv_batch='/home/user/Documents/GitHub/openptv2/.venv/bin/pyptv_batch'

# Reload shell profile
source ~/.bashrc

# Now launch cleanly from any folder
pyptv_gui -w ./test_data/test_cavity

Single Runtime Behavior

OpenPTV2 now ships a single runtime:

  1. One codebase: algorithms/*.py is the only implementation path.
  2. Two execution modes: the same modules run interpreted during development and compiled when built through Cython 3.
  3. No engine switching: OPENPTV_ENGINE, set_engine(), and optv dispatching are no longer part of the runtime model.

Short vs. Long Options

Both styles are fully supported across all command-line scripts. Choose based on your context:

  • Short Options (-e, -w, -f, -l): Best for manual terminal use, quick debugging, and live interactive typing.
  • Long Options (--engine, --workdir, --first, --last): Best for automation scripts, documentation, and config files to maximize readability.

Documentation

Repository Structure

openptv2/
├── src/openptv2/
│   ├── algorithms/    # Cython 3 pure-Python engine: calibration,
│   │                  # correspondences, orientation, tracking, etc.
│   │                  # (the only algorithm implementation path)
│   ├── plugins/       # Pluggable tracker/sequence implementations
│   │                  # (fast, MyPTV/ProPTV-inspired trackers, rembg, ...)
│   ├── batch/         # openptv2-batch / pyptv_batch headless pipeline
│   ├── storage/       # Zarr frame store (res/run.zarr)
│   ├── gui/           # TraitsUI/Chaco desktop application
│   ├── tracker.py, calibration.py, correspondences.py, ...  # public API
│   └── __init__.py
├── tests/             # Test suite (unit, parity, perf, integration, gui)
├── docs/              # Documentation, tutorials, developer guide
├── scripts/           # Build/analysis/benchmark helper scripts
├── docker/            # Dockerfiles for GUI and cloud batch images
├── test_data/         # Calibration files, parameter files, fixtures
├── pyproject.toml     # Python project config (build, deps, entry points)
└── README.md

Testing

# All tests
uv run pytest

# By marker
uv run pytest -m unit
uv run pytest -m "not slow"

# GUI tests (headless)
uv run pytest tests/gui/ -v

# Hot-path smoke test (tracking + correspondences)
uv run pytest tests/unit/test_track.py tests/unit/test_track3d.py tests/unit/test_correspondences.py -v --tb=short

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Run tests: pytest
  5. Submit a pull request

Development Workflow

# Set up development environment
uv sync --extra dev

# Make changes to source code

# Run tests
pytest tests/ -v

# Build documentation (optional)
cd docs && make html

License

MIT. See LICENSE for details.

As of 0.3.2, openptv2 is relicensed from LGPL-3.0 to MIT. The C/Cython core that carried the LGPL license now lives in the separate openptv repo; this codebase is a from-scratch pure-Python/Cython PTV engine plus a pluggable tracker architecture.

Third-party tracking code

openptv2 includes and adapts tracking code from two MIT-licensed projects, in accordance with their licenses:

  • MyPTV (MIT, © 2022 Ron Shnapp) — algorithm concepts adapted into the myptv_2d_tracking / myptv_3d_tracking plugins. Their MIT notice is incorporated; see https://github.com/ronshnapp/MyPTV.

  • proPTV (MIT, © 2023 DLR / Robin Barta) — the GMM / Savitzky-Golay routines in src/openptv2/plugins/proptv/ are vendored from proPTV. proPTV's license requires that its copyright/permission notice be included in copies and that its underlying publication be cited. Accordingly:

    MIT License, Copyright (c) 2023 DLR (Project owner: Robin Barta). GMM / Savitzky-Golay routines adapted from Barta, Robin, et al. "proPTV – A probabilistic particle tracking velocimetry framework." Journal of Computational Physics (2024), https://doi.org/10.1016/j.jcp.2024.113212.

    The vendored modules at src/openptv2/plugins/proptv/ carry the MIT copyright/permission notice in their headers.

Using either project's advanced, project-specific features is out of scope here — refer to and use those projects directly.

Acknowledgments

openptv2 combines work from:

  • openptv - C library and bindings
  • pyptv - Python GUI
  • openptv-python - Python/Numba engine
  • MyPTV - 2D/3D tracking algorithm concepts adapted into the MyPTV plugins (MIT)
  • proPTV - GMM / Savitzky-Golay track-prediction routines vendored into src/openptv2/plugins/proptv/ (MIT, © 2023 DLR / Robin Barta)

See License for the licensing and citation details.

Contact

Helper Scripts

The project includes scripts for building and testing:

Script Purpose
scripts/build_wheel.sh Build binary wheel from source
scripts/install_wheel.sh Install wheel in clean test environment
scripts/run_tests.sh Run test suite in test environment
docker/Dockerfile.slim Slim Docker image for testing

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.5.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for alexlib from gravatar.com alexlib

Credits

Author: OpenPTV Community

License expression

MIT
View SPDX License List

Requires

Python <3.15, >=3.11

Provides Extra

gui rembg viz dev

Classifiers

Report project as malware

Download files

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

Source Distribution

openptv2-0.4.1.tar.gz (5.6 MB view details)

Uploaded Source

Built Distributions

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

openptv2-0.4.1-cp313-cp313-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.13Windows x86-64

openptv2-0.4.1-cp313-cp313-musllinux_1_2_x86_64.whl (25.9 MB view details)

Uploaded CPython 3.13musllinux: musl 1.2+ x86-64

openptv2-0.4.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (26.0 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

openptv2-0.4.1-cp313-cp313-macosx_11_0_arm64.whl (9.1 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

openptv2-0.4.1-cp312-cp312-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.12Windows x86-64

openptv2-0.4.1-cp312-cp312-musllinux_1_2_x86_64.whl (26.0 MB view details)

Uploaded CPython 3.12musllinux: musl 1.2+ x86-64

openptv2-0.4.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (26.1 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

openptv2-0.4.1-cp312-cp312-macosx_11_0_arm64.whl (9.1 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

openptv2-0.4.1-cp311-cp311-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.11Windows x86-64

openptv2-0.4.1-cp311-cp311-musllinux_1_2_x86_64.whl (26.3 MB view details)

Uploaded CPython 3.11musllinux: musl 1.2+ x86-64

openptv2-0.4.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (26.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

openptv2-0.4.1-cp311-cp311-macosx_11_0_arm64.whl (9.1 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

File details

Details for the file openptv2-0.4.1.tar.gz.

File metadata

  • Download URL: openptv2-0.4.1.tar.gz
  • Upload date:
  • Size: 5.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for openptv2-0.4.1.tar.gz
Algorithm Hash digest
SHA256 e555b5f94596fd3f85e247ca978e20f4bfd0312d19e6d7b435ca8821462ff491
MD5 7d25de5eaf6638aae50e68542f5e4580
BLAKE2b-256 c0c72c21e959f041b95df981d22aff72dbd0b4f67ce9d552ba8b57d63144a890

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1.tar.gz:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: openptv2-0.4.1-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 8.2 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 openptv2-0.4.1-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 d4534db29ebe59fe6374c8927c16ca5bad28b3d8ff7a7be5641e5ff4c9246a22
MD5 284f001c8d9747add320ecfbf5621d91
BLAKE2b-256 5d259f0c5695fe4a782c1fee20882b06415f2fe15fbcaad46032d9d119546ba1

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp313-cp313-win_amd64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp313-cp313-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp313-cp313-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 72bf0a39c1429973d87e3d37cb1969f8fa06407969b4408080e2b206c74d9562
MD5 c3f4efd1d1f624dff102ab299b3653d2
BLAKE2b-256 a762241d51d1fd6c0ffb49eec9f54d11a7e13af5796dd84d7359bdd2b8b1f0a2

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp313-cp313-musllinux_1_2_x86_64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f98482b8a9d27d6c4e0107e47106f93f67f2e404e101b6bf9ccb3cf4d5acf0fe
MD5 7800af192e7942dee7385dbfffab603e
BLAKE2b-256 8e3c2945a034259feef937ada2efd561b6d05a861c90784fca8dc49ce188d53b

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9dbbaeae085a03beb64a1e3e3b65e9273083d543b7ebdf0e09d1775f5078ed2d
MD5 3b301b111336b6b5514e10a3bc0f54b5
BLAKE2b-256 ad24c99be6b2e4c652e3702ad7d83cf010a04416e54d940d9c6ac12f9ede1f15

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp313-cp313-macosx_11_0_arm64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: openptv2-0.4.1-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 8.2 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 openptv2-0.4.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 3559ffff887db2dc75f807859318bbd4596cdbc1de7c9b83c3bc210dec91bfac
MD5 48659abfe94850e08f8c7ab5e327dc9e
BLAKE2b-256 741a25e17d02837faf33522b6cf30a68b43c7dfa5627df42416382be93490ab0

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp312-cp312-win_amd64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp312-cp312-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp312-cp312-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 ddaab10f15e76f25f47b862f914a3b7e023fd563e8005034f099072aa4e73221
MD5 40021b981cd12be1ee2f9a90ecf4df23
BLAKE2b-256 c2cddc7e46b010204477c4d1fc06dafb25b5946ee99f8f422da499f4a1b138bb

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp312-cp312-musllinux_1_2_x86_64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e1d86c6039be8ef284ccf1b39da4a8173cea15493819b11b587c3be8d1f16634
MD5 a7b19d4cdf5c33ff35078840c1798146
BLAKE2b-256 3816efff7a3d50cae0dab847fc2cc5ed3bb4b1b7b2d19575eb44abbfbb7a00fd

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1b4f9d867faeb871d1bbd366ba5c0e4ea7c1b7e5e0474b3631e2892b1bc340d0
MD5 ee7c66b4f53173d1cc5ea6ae301feff6
BLAKE2b-256 4b349cb980a50839143737c47ae66badef2bb1b2e1be5c4e8797836b7c99dc8e

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp312-cp312-macosx_11_0_arm64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: openptv2-0.4.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 8.2 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 openptv2-0.4.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 f014f797a0d4a64b7489249d9d8ee056f78afaf98481712f2a9cf0d25a453f00
MD5 314690eef8bb04d65f400e9af275a35c
BLAKE2b-256 d894a829a31e0f1455ea7c45b8ec81db8d75b2f9fba18bd8aba80d1acb06e7a9

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp311-cp311-win_amd64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp311-cp311-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp311-cp311-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 5c21f7e42f36661d012afdcfea2a903992f5d2b1943ee9f29fb284ed94192e43
MD5 4b21da40d29ae79b16d0bfc2dbf08dc8
BLAKE2b-256 0e42db32f5aca1cbc2186cc50c8a7f0777c1586fc59d903f59a915fdb910606c

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp311-cp311-musllinux_1_2_x86_64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1dc1965171030cc734673d19cab3cba13df286d16a0635e2c3a8a4670c693c37
MD5 c252778e5404a9d586b22b810090818d
BLAKE2b-256 6600332687ecb3566d0c861a7c27ad519d9506c932b4fe3ffe5f6bc2a3433504

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openptv2-0.4.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for openptv2-0.4.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cba3b2f5e84cc32b9dfcb75fc94f6f5b26acb21182663765d19029449882f2ce
MD5 4be2246898b5f5c6779a2672a88928bf
BLAKE2b-256 b9d78a27e80d190e5a9bec02308936aa26c1d772985f7636b1a60b30e9a1a104

See more details on using hashes here.

Provenance

The following attestation bundles were made for openptv2-0.4.1-cp311-cp311-macosx_11_0_arm64.whl:

Publisher: cibuildwheel.yml on alexlib/openptv2

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

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