Installation

Compiling

The library uses c++11 and requires Boost with version 1.48.0 or more recent. It is a multi-platform library and compiles on Linux, Mac OSX and Visual Studio 2015. It also requires cmake to generate makefiles, and cython to compile the library.

On Windows , only Python 3.5 and 3.6 are available because of the required Visual Studio version.

On other systems, if you have several Python/cython 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 Cythonization

To build the GUDHI cython module, run the following commands in a terminal:

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

Test suites

To test your build, py.test is optional. Run the following command in a terminal:

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

Documentation

To build the documentation, sphinx-doc is required. Please refer to conf.py file to see which sphinx-doc modules are required to generate the documentation. Run the following commands in a terminal:

make sphinx

Optional third-party library

CGAL

The Alpha complex, Tangential complex and Witness complex data structures, and Bottleneck distance requires CGAL, which is a C++ library which provides easy access to efficient and reliable geometric algorithms.

Having CGAL, the Computational Geometry Algorithms Library, version 4.7.0 or higher installed is recommended. The procedure to install this library according to your operating system is detailed here.

The following examples requires CGAL version ≥ 4.7.0:

• alpha_complex_diagram_persistence_from_off_file_example.py
• alpha_complex_from_points_example.py

The following examples requires CGAL version ≥ 4.8.0:

• bottleneck_basic_example.py
• tangential_complex_plain_homology_from_off_file_example.py

The following examples requires CGAL version ≥ 4.8.1:

• euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py
• euclidean_witness_complex_diagram_persistence_from_off_file_example.py

Eigen3

The Alpha complex, Tangential complex and Witness complex data structures and few examples requires Eigen3, a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.

The following examples require the Eigen3:

• alpha_complex_diagram_persistence_from_off_file_example.py
• alpha_complex_from_points_example.py
• tangential_complex_plain_homology_from_off_file_example.py
• euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py
• euclidean_witness_complex_diagram_persistence_from_off_file_example.py

Matplotlib

The persistence graphical tools module requires Matplotlib, a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.

The following examples require the Matplotlib:

• alpha_complex_diagram_persistence_from_off_file_example.py
• gudhi_graphical_tools_example.py
• periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py
• rips_complex_diagram_persistence_from_off_file_example.py
• rips_persistence_diagram.py
• rips_complex_diagram_persistence_from_distance_matrix_file_example.py
• tangential_complex_plain_homology_from_off_file_example.py
• euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py
• euclidean_witness_complex_diagram_persistence_from_off_file_example.py

Numpy

The persistence graphical tools module requires NumPy, a fundamental package for scientific computing with Python.

The following examples require the NumPy:

• alpha_complex_diagram_persistence_from_off_file_example.py
• gudhi_graphical_tools_example.py
• periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py
• rips_complex_diagram_persistence_from_off_file_example.py
• rips_persistence_diagram.py
• rips_complex_diagram_persistence_from_distance_matrix_file_example.py
• tangential_complex_plain_homology_from_off_file_example.py
• euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py
• euclidean_witness_complex_diagram_persistence_from_off_file_example.py

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.

Bug reports and contributions

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

Contact: gudhi-users@lists.gforge.inria.fr

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