Installation

Conda

The easiest way to install the Python version of GUDHI is using conda.

Compiling

The library uses c++14 and requires Boost ≥ 1.56.0, CMake ≥ 3.1 to generate makefiles, NumPy, Cython and pybind11 to compile the GUDHI Python module. It is a multi-platform library and compiles on Linux, Mac OSX and Visual Studio 2017.

On Windows , only Python ≥ 3.5 are available because of the required Visual Studio version.

On other systems, if you have several Python/python installed, the version 2.X will be used by default, but you can force it by adding -DPython_ADDITIONAL_VERSIONS=3 to the cmake command.

GUDHI Python module compilation

After making sure that the Compilation dependencies are properly installed, one can build the GUDHI Python module, by running the following commands in a terminal:

cd /path-to-gudhi/
mkdir build
cd build/
cmake ..
cd python
make

Note

make python (or make in python directory) is only a CMake custom targets to shortcut python setup.py build_ext --inplace command. No specific other options (-j8 for parallel, or even make clean, …) are available. But one can use python setup.py ... specific options in the python directory:

python setup.py clean --all               # Clean former compilation
python setup.py build_ext -j 8 --inplace  # Build in parallel

GUDHI Python module installation

Once the compilation succeeds, one can add the GUDHI Python module path to the PYTHONPATH:

# For windows, you have to set PYTHONPATH environment variable
export PYTHONPATH='$PYTHONPATH:/path-to-gudhi/build/python'

Or install it definitely in your Python packages folder:

cd /path-to-gudhi/build/python
# May require sudo or administrator privileges
make install

Note

make install is only a CMake custom targets to shortcut python setup.py install command. It does not take into account CMAKE_INSTALL_PREFIX. But one can use python setup.py install ... specific options in the python directory:

python setup.py install --prefix /home/gudhi  # Install in /home/gudhi directory

Test suites

To test your build, py.test is required. Run the following Ctest (CMake test driver program) command in a terminal:

cd /path-to-gudhi/build/python
# For windows, you have to set PYTHONPATH environment variable
export PYTHONPATH='$PYTHONPATH:/path-to-gudhi/build/python'
ctest

Note

One can use ctest specific options in the python directory:

# Launch tests in parallel on 8 cores and set failing tests in verbose mode
ctest -j 8 --output-on-failure

Debugging issues

If tests fail, please check your PYTHONPATH and try to import gudhi and check the errors. The problem can come from a third-party library bad link or installation.

If import gudhi succeeds, please have a look to debug information:

import gudhi
print(gudhi.__debug_info__)

You shall have something like:

Python version 2.7.15
Cython version 0.26.1
Numpy version 1.14.1
Eigen3 version 3.1.1
Installed modules are: off_reader;simplex_tree;rips_complex;
    cubical_complex;periodic_cubical_complex;reader_utils;witness_complex;
    strong_witness_complex;alpha_complex;
Missing modules are: bottleneck_distance;nerve_gic;subsampling;
    tangential_complex;persistence_graphical_tools;
    euclidean_witness_complex;euclidean_strong_witness_complex;
CGAL version 4.7.1000
GMP_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmp.so
GMPXX_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmpxx.so
TBB version 9107 found and used

Here, you can see that bottleneck_distance, nerve_gic, subsampling and tangential_complex are missing because of the CGAL version. persistence_graphical_tools is not available as matplotlib is not available. Unitary tests cannot be run as pytest is missing.

A complete configuration would be :

Python version 3.6.5
Cython version 0.28.2
Pytest version 3.3.2
Matplotlib version 2.2.2
Numpy version 1.14.5
Eigen3 version 3.3.4
Installed modules are: off_reader;simplex_tree;rips_complex;
    cubical_complex;periodic_cubical_complex;persistence_graphical_tools;
    reader_utils;witness_complex;strong_witness_complex;
    persistence_graphical_tools;bottleneck_distance;nerve_gic;subsampling;
    tangential_complex;alpha_complex;euclidean_witness_complex;
    euclidean_strong_witness_complex;
CGAL header only version 4.11.0
GMP_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmp.so
GMPXX_LIBRARIES = /usr/lib/x86_64-linux-gnu/libgmpxx.so
TBB version 9107 found and used

Documentation

To build the documentation, sphinx-doc and sphinxcontrib-bibtex are required. As the documentation is auto-tested, CGAL, Eigen, Matplotlib, NumPy, POT, Scikit-learn and SciPy are also mandatory to build the documentation.

Run the following commands in a terminal:

cd /path-to-gudhi/build/python
make sphinx

Optional third-party library

Compilation dependencies

These third party dependencies are detected by CMake. They have to be installed before performing the GUDHI Python module compilation.

CGAL

Some GUDHI modules (cf. modules list), and few examples require CGAL, a C++ library that provides easy access to efficient and reliable geometric algorithms.

The procedure to install this library according to your operating system is detailed here.

The following examples require CGAL version ≥ 4.11.0:

Eigen

Some GUDHI modules (cf. modules list), and few examples require Eigen, a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.

The following examples require Eigen version ≥ 3.1.0:

Threading Building Blocks

Intel® TBB lets you easily write parallel C++ programs that take full advantage of multicore performance, that are portable and composable, and that have future-proof scalability.

Having Intel® TBB installed is recommended to parallelize and accelerate some GUDHI computations.

Run time dependencies

These third party dependencies are detected by Python import mechanism at run time. They can be installed when required.

EagerPy

Some Python functions can handle automatic differentiation (possibly only when a flag enable_autodiff=True is used). In order to reduce code duplication, we use EagerPy which wraps arrays from PyTorch, TensorFlow and JAX in a common interface.

Hnswlib

KNearestNeighbors can use the Python package Hnswlib as a backend if explicitly requested, to speed-up queries.

PyKeOps

KNearestNeighbors can use the Python package PyKeOps as a backend if explicitly requested, to speed-up queries using a GPU.

Python Optimal Transport

The Wasserstein distance module requires POT, a library that provides several solvers for optimization problems related to Optimal Transport.

PyTorch

PyTorch is currently only used as a dependency of PyKeOps, and in some tests.

Scikit-learn

The persistence representations module require scikit-learn, a Python-based ecosystem of open-source software for machine learning.

KNearestNeighbors can use the Python package scikit-learn as a backend if explicitly requested.

SciPy

The persistence graphical tools and Wasserstein distance modules require SciPy, a Python-based ecosystem of open-source software for mathematics, science, and engineering.

KNearestNeighbors can use the Python package SciPy as a backend if explicitly requested.

Bug reports and contributions

Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to:

GUDHI is open to external contributions. If you want to join our development team, please contact us.