volumembo is a python package providing an implementation of an "efficient volume-preserving MBO scheme for data clustering and classification" discussed in Krämer, Laux, arXiv:2412.17694 (2024). volumembo also provides a C++ backend with an efficient implementation of the volume-preserving MBO scheme.
Using volumembo requires the following software installed:
- Python
>= 3.10
In order to build the package from source, the following tools are also needed:
- A C++20-compliant compiler
The following sequence of commands is used to build volumeMBO from source:
git clone https://github.com/fabiuskt/VolumeMBO_OS
cd VolumeMBO_OS
python -m pip install .
⚡ Optional: Faster installation using uv
uv is a drop-in replacement for pip and virtualenv that provides extremely fast package installation and dependency resolution.
python -m uv pip install .
If you intend to contribute to the development of volumembo, we recommend a locally compiled version using these instructions:
git clone --recurse-submodules https://github.com/fabiuskt/VolumeMBO_OS
cd VolumeMBO_OS
python -m uv pip install -r requirements-dev.txt
python -m uv pip install --no-build-isolation --config-settings=build-dir="build" --config-settings=cmake.define.BUILD_TESTING="ON" -v -e .
This clones the repository including all submodules, installs the volumembo package, exposes the CMake build directory as build, and builds tests. The -e (editable) flag allows the change of Python sources of volumembo without reinstalling the package. The -v flag enables verbose output which gives you detailed information about the compilation process that you should include into potential bug reports. To recompile the C++ source, please re-run uv pip install.
The API documentation can be built with
python -m uv pip install --no-build-isolation --config-settings=build-dir="build" --config-settings=cmake.define.BUILD_DOCS="ON" -v -e .
cmake --build build --target sphinx-doc
and can then be found in build/doc/sphinx/index.html.
Krämer, F., and Laux, T., "An efficient volume-preserving MBO scheme for data clustering and classification." arXiv:2412.17694 (2024). DOI: 10.48550/arXiv.2412.17694
See LICENSE.md.