diff options
Diffstat (limited to 'cython')
111 files changed, 0 insertions, 9787 deletions
diff --git a/cython/CMakeLists.txt b/cython/CMakeLists.txt deleted file mode 100644 index 09ea28f1..00000000 --- a/cython/CMakeLists.txt +++ /dev/null @@ -1,405 +0,0 @@ -project(Cython) - -function( add_gudhi_cython_lib THE_LIB ) - if(EXISTS ${THE_LIB}) - get_filename_component(THE_LIB_FILE_NAME ${THE_LIB} NAME_WE) - if(WIN32) - message("++ ${THE_LIB} => THE_LIB_FILE_NAME = ${THE_LIB_FILE_NAME}") - set(GUDHI_CYTHON_LIBRARIES "${GUDHI_CYTHON_LIBRARIES}'${THE_LIB_FILE_NAME}', " PARENT_SCOPE) - else(WIN32) - STRING(REGEX REPLACE "lib" "" UNIX_LIB_FILE_NAME ${THE_LIB_FILE_NAME}) - message("++ ${THE_LIB} => UNIX_LIB_FILE_NAME = ${UNIX_LIB_FILE_NAME}") - set(GUDHI_CYTHON_LIBRARIES "${GUDHI_CYTHON_LIBRARIES}'${UNIX_LIB_FILE_NAME}', " PARENT_SCOPE) - endif(WIN32) - endif(EXISTS ${THE_LIB}) -endfunction( add_gudhi_cython_lib ) - -# THE_TEST is the python test file name (without .py extension) containing tests functions -function( add_gudhi_py_test THE_TEST ) - if(PYTEST_FOUND) - # use ${PYTHON_EXECUTABLE} -B, otherwise a __pycache__ directory is created in sources by python - # use py.test no cache provider, otherwise a .cache file is created in sources by py.test - add_test(NAME ${THE_TEST}_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} -B -m pytest -p no:cacheprovider ${CMAKE_CURRENT_SOURCE_DIR}/test/${THE_TEST}.py) - endif() -endfunction( add_gudhi_py_test ) - -# Set gudhi.__debug_info__ -# WARNING : to be done before gudhi.pyx.in configure_file -function( add_gudhi_debug_info DEBUG_INFO ) - set(GUDHI_CYTHON_DEBUG_INFO "${GUDHI_CYTHON_DEBUG_INFO} \"${DEBUG_INFO}\\n\" \\\n" PARENT_SCOPE) -endfunction( add_gudhi_debug_info ) - - -if(CYTHON_FOUND) - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}off_reader;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}simplex_tree;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}rips_complex;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}cubical_complex;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}periodic_cubical_complex;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}persistence_graphical_tools;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}reader_utils;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}witness_complex;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}strong_witness_complex;") - - add_gudhi_debug_info("Python version ${PYTHON_VERSION_STRING}") - add_gudhi_debug_info("Cython version ${CYTHON_VERSION}") - if(PYTEST_FOUND) - add_gudhi_debug_info("Pytest version ${PYTEST_VERSION}") - endif() - if(MATPLOTLIB_FOUND) - add_gudhi_debug_info("Matplotlib version ${MATPLOTLIB_VERSION}") - endif() - if(NUMPY_FOUND) - add_gudhi_debug_info("Numpy version ${NUMPY_VERSION}") - endif() - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}persistence_graphical_tools;") - else() - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MODULES}persistence_graphical_tools;") - endif() - - message("++ ${PYTHON_EXECUTABLE} v.${PYTHON_VERSION_STRING} - Cython is ${CYTHON_VERSION} - Sphinx is ${SPHINX_PATH}") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_RESULT_OF_USE_DECLTYPE', ") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_ALL_NO_LIB', ") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DBOOST_SYSTEM_NO_DEPRECATED', ") - - # Gudhi and CGAL compilation option - if(MSVC) - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'/fp:strict', ") - else(MSVC) - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-std=c++11', ") - endif(MSVC) - if(CMAKE_COMPILER_IS_GNUCXX) - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-frounding-math', ") - endif(CMAKE_COMPILER_IS_GNUCXX) - if (CMAKE_CXX_COMPILER_ID MATCHES Intel) - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-fp-model strict', ") - endif(CMAKE_CXX_COMPILER_ID MATCHES Intel) - if (DEBUG_TRACES) - # For programs to be more verbose - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DDEBUG_TRACES', ") - endif() - - if (EIGEN3_FOUND) - add_gudhi_debug_info("Eigen3 version ${EIGEN3_VERSION}") - # No problem, even if no CGAL found - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_EIGEN3_ENABLED', ") - endif (EIGEN3_FOUND) - - if (NOT CGAL_VERSION VERSION_LESS 4.8.1) - set(GUDHI_CYTHON_BOTTLENECK_DISTANCE "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/bottleneck_distance.pyx'") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}bottleneck_distance;") - set(GUDHI_CYTHON_NERVE_GIC "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/nerve_gic.pyx'") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}nerve_gic;") - else() - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MISSING_MODULES}bottleneck_distance;") - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MISSING_MODULES}nerve_gic;") - endif () - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) - set(GUDHI_CYTHON_SUBSAMPLING "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/subsampling.pyx'") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}subsampling;") - set(GUDHI_CYTHON_TANGENTIAL_COMPLEX "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/tangential_complex.pyx'") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}tangential_complex;") - else() - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MISSING_MODULES}subsampling;") - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MISSING_MODULES}tangential_complex;") - endif () - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) - set(GUDHI_CYTHON_ALPHA_COMPLEX "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/alpha_complex.pyx'") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}alpha_complex;") - else() - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MISSING_MODULES}alpha_complex;") - endif () - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) - set(GUDHI_CYTHON_EUCLIDEAN_WITNESS_COMPLEX - "include '${CMAKE_CURRENT_SOURCE_DIR}/cython/euclidean_witness_complex.pyx'\ninclude '${CMAKE_CURRENT_SOURCE_DIR}/cython/euclidean_strong_witness_complex.pyx'\n") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}euclidean_witness_complex;") - set(GUDHI_CYTHON_MODULES "${GUDHI_CYTHON_MODULES}euclidean_strong_witness_complex;") - else() - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MISSING_MODULES}euclidean_witness_complex;") - set(GUDHI_CYTHON_MISSING_MODULES "${GUDHI_CYTHON_MISSING_MODULES}euclidean_strong_witness_complex;") - endif () - - add_gudhi_debug_info("Installed modules are: ${GUDHI_CYTHON_MODULES}") - if(GUDHI_CYTHON_MISSING_MODULES) - add_gudhi_debug_info("Missing modules are: ${GUDHI_CYTHON_MISSING_MODULES}") - endif() - - if(CGAL_FOUND) - can_cgal_use_cxx11_thread_local() - if (NOT CGAL_CAN_USE_CXX11_THREAD_LOCAL_RESULT) - if(CMAKE_BUILD_TYPE MATCHES Debug) - add_gudhi_cython_lib("${Boost_THREAD_LIBRARY_DEBUG}") - else() - add_gudhi_cython_lib("${Boost_THREAD_LIBRARY_RELEASE}") - endif() - set(GUDHI_CYTHON_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}'${Boost_LIBRARY_DIRS}', ") - endif() - # Add CGAL compilation args - if(CGAL_HEADER_ONLY) - add_gudhi_debug_info("CGAL header only version ${CGAL_VERSION}") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_HEADER_ONLY', ") - else(CGAL_HEADER_ONLY) - add_gudhi_debug_info("CGAL version ${CGAL_VERSION}") - add_gudhi_cython_lib("${CGAL_LIBRARY}") - set(GUDHI_CYTHON_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}'${CGAL_LIBRARIES_DIR}', ") - # If CGAL is not header only, CGAL library may link with boost system, - if(CMAKE_BUILD_TYPE MATCHES Debug) - add_gudhi_cython_lib("${Boost_SYSTEM_LIBRARY_DEBUG}") - else() - add_gudhi_cython_lib("${Boost_SYSTEM_LIBRARY_RELEASE}") - endif() - set(GUDHI_CYTHON_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}'${Boost_LIBRARY_DIRS}', ") - endif(CGAL_HEADER_ONLY) - # GMP and GMPXX are not required, but if present, CGAL will link with them. - if(GMP_FOUND) - add_gudhi_debug_info("GMP_LIBRARIES = ${GMP_LIBRARIES}") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_USE_GMP', ") - add_gudhi_cython_lib("${GMP_LIBRARIES}") - set(GUDHI_CYTHON_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}'${GMP_LIBRARIES_DIR}', ") - if(GMPXX_FOUND) - add_gudhi_debug_info("GMPXX_LIBRARIES = ${GMPXX_LIBRARIES}") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DCGAL_USE_GMPXX', ") - add_gudhi_cython_lib("${GMPXX_LIBRARIES}") - set(GUDHI_CYTHON_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}'${GMPXX_LIBRARIES_DIR}', ") - endif(GMPXX_FOUND) - endif(GMP_FOUND) - endif(CGAL_FOUND) - - # Specific for Mac - if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-mmacosx-version-min=10.12', ") - set(GUDHI_CYTHON_EXTRA_LINK_ARGS "${GUDHI_CYTHON_EXTRA_LINK_ARGS}'-mmacosx-version-min=10.12', ") - endif(${CMAKE_SYSTEM_NAME} MATCHES "Darwin") - - # Loop on INCLUDE_DIRECTORIES PROPERTY - get_property(GUDHI_INCLUDE_DIRECTORIES DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} PROPERTY INCLUDE_DIRECTORIES) - foreach(GUDHI_INCLUDE_DIRECTORY ${GUDHI_INCLUDE_DIRECTORIES}) - set(GUDHI_CYTHON_INCLUDE_DIRS "${GUDHI_CYTHON_INCLUDE_DIRS}'${GUDHI_INCLUDE_DIRECTORY}', ") - endforeach() - set(GUDHI_CYTHON_INCLUDE_DIRS "${GUDHI_CYTHON_INCLUDE_DIRS}'${CMAKE_SOURCE_DIR}/${GUDHI_CYTHON_PATH}/include', ") - - if (TBB_FOUND AND WITH_GUDHI_USE_TBB) - add_gudhi_debug_info("TBB version ${TBB_INTERFACE_VERSION} found and used") - set(GUDHI_CYTHON_EXTRA_COMPILE_ARGS "${GUDHI_CYTHON_EXTRA_COMPILE_ARGS}'-DGUDHI_USE_TBB', ") - if(CMAKE_BUILD_TYPE MATCHES Debug) - add_gudhi_cython_lib("${TBB_DEBUG_LIBRARY}") - add_gudhi_cython_lib("${TBB_MALLOC_DEBUG_LIBRARY}") - else() - add_gudhi_cython_lib("${TBB_RELEASE_LIBRARY}") - add_gudhi_cython_lib("${TBB_MALLOC_RELEASE_LIBRARY}") - endif() - set(GUDHI_CYTHON_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}'${TBB_LIBRARY_DIRS}', ") - set(GUDHI_CYTHON_INCLUDE_DIRS "${GUDHI_CYTHON_INCLUDE_DIRS}'${TBB_INCLUDE_DIRS}', ") - endif() - - if(UNIX) - set( GUDHI_CYTHON_RUNTIME_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}") - endif(UNIX) - - # Generate setup.py file to cythonize Gudhi - This file must be named setup.py by convention - configure_file(setup.py.in "${CMAKE_CURRENT_BINARY_DIR}/setup.py" @ONLY) - # Generate gudhi.pyx - Gudhi cython file - configure_file(gudhi.pyx.in "${CMAKE_CURRENT_BINARY_DIR}/gudhi.pyx" @ONLY) - - add_custom_command( - OUTPUT gudhi.so - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/setup.py" "build_ext" "--inplace") - - add_custom_target(cython ALL DEPENDS gudhi.so - COMMENT "Do not forget to add ${CMAKE_CURRENT_BINARY_DIR}/ to your PYTHONPATH before using examples or tests") - - # For installation purpose - # TODO(VR) : files matching pattern mechanism is copying all cython directory - install(DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}" DESTINATION "${PYTHON_SITE_PACKAGES}/" FILES_MATCHING - PATTERN "*.so" - PATTERN "*.dylib" - PATTERN "*.pyd") - - # Test examples - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) - # Bottleneck and Alpha - add_test(NAME alpha_rips_persistence_bottleneck_distance_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_rips_persistence_bottleneck_distance.py" - -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -t 0.15 -d 3) - - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) - # Tangential - add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/tangential_complex_plain_homology_from_off_file_example.py" - --no-diagram -i 2 -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off) - - add_gudhi_py_test(test_tangential_complex) - - # Witness complex AND Subsampling - add_test(NAME euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) - - add_test(NAME euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2) - endif() - - # Subsampling - add_gudhi_py_test(test_subsampling) - - endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) - if (NOT CGAL_VERSION VERSION_LESS 4.8.1) - # Bottleneck - add_test(NAME bottleneck_basic_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/bottleneck_basic_example.py") - - add_gudhi_py_test(test_bottleneck_distance) - - # Cover complex - file(COPY ${CMAKE_SOURCE_DIR}/data/points/human.off DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) - file(COPY ${CMAKE_SOURCE_DIR}/data/points/COIL_database/lucky_cat.off DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) - file(COPY ${CMAKE_SOURCE_DIR}/data/points/COIL_database/lucky_cat_PCA1 DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) - add_test(NAME cover_complex_nerve_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/nerve_of_a_covering.py" - -f human.off -c 2 -r 10 -g 0.3) - - add_test(NAME cover_complex_coordinate_gic_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/coordinate_graph_induced_complex.py" - -f human.off -c 0 -v) - - add_test(NAME cover_complex_functional_gic_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/functional_graph_induced_complex.py" - -o lucky_cat.off - -f lucky_cat_PCA1 -v) - - add_test(NAME cover_complex_voronoi_gic_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/voronoi_graph_induced_complex.py" - -f human.off -n 700 -v) - - add_gudhi_py_test(test_cover_complex) - endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) - - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) - # Alpha - add_test(NAME alpha_complex_from_points_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_from_points_example.py") - - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) - add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_diagram_persistence_from_off_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 0.6) - endif() - - add_gudhi_py_test(test_alpha_complex) - - endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) - - if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) - # Euclidean witness - add_gudhi_py_test(test_euclidean_witness_complex) - - endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) - - # Cubical - add_test(NAME periodic_cubical_complex_barcode_persistence_from_perseus_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py" - --no-barcode -f ${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt) - - if(NUMPY_FOUND) - add_test(NAME random_cubical_complex_persistence_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/random_cubical_complex_persistence_example.py" - 10 10 10) - endif() - - add_gudhi_py_test(test_cubical_complex) - - # Rips - if(MATPLOTLIB_FOUND AND NUMPY_FOUND) - add_test(NAME rips_complex_diagram_persistence_from_distance_matrix_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py" - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3) - - add_test(NAME rips_complex_diagram_persistence_from_off_file_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_off_file_example.py - --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -e 0.25 -d 3) - endif() - - add_test(NAME rips_complex_from_points_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_from_points_example.py) - - add_gudhi_py_test(test_rips_complex) - - # Simplex tree - add_test(NAME simplex_tree_example_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/simplex_tree_example.py) - - add_gudhi_py_test(test_simplex_tree) - - # Witness - add_test(NAME witness_complex_from_nearest_landmark_table_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/witness_complex_from_nearest_landmark_table.py) - - add_gudhi_py_test(test_witness_complex) - - # Reader utils - add_gudhi_py_test(test_reader_utils) - - # Documentation generation is available through sphinx - requires all modules - if(SPHINX_PATH AND MATPLOTLIB_FOUND AND NUMPY_FOUND AND NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) - set (GUDHI_SPHINX_MESSAGE "Generating API documentation with Sphinx in ${CMAKE_CURRENT_BINARY_DIR}/sphinx/") - # User warning - Sphinx is a static pages generator, and configured to work fine with user_version - # Images and biblio warnings because not found on developper version - if (GUDHI_CYTHON_PATH STREQUAL "src/cython") - set (GUDHI_SPHINX_MESSAGE "${GUDHI_SPHINX_MESSAGE} \n WARNING : Sphinx is configured for user version, you run it on developper version. Images and biblio will miss") - endif() - # sphinx target requires gudhi.so, because conf.py reads gudhi version from it - add_custom_target(sphinx - WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/doc - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${SPHINX_PATH} -b html ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/sphinx - DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/gudhi.so" - COMMENT "${GUDHI_SPHINX_MESSAGE}" VERBATIM) - - add_test(NAME sphinx_py_test - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" - ${SPHINX_PATH} -b doctest ${CMAKE_CURRENT_SOURCE_DIR}/doc ${CMAKE_CURRENT_BINARY_DIR}/doctest) - - endif() -endif(CYTHON_FOUND) diff --git a/cython/CONVENTIONS b/cython/CONVENTIONS deleted file mode 100644 index 804e97f3..00000000 --- a/cython/CONVENTIONS +++ /dev/null @@ -1,9 +0,0 @@ -Gudhi is following PEP8 conventions. - -Please refer to: -https://www.python.org/dev/peps/pep-0008/ - -A summary: - - modules (filenames) should have short, all-lowercase names, and they can contain underscores. - - packages (directories) should have short, all-lowercase names, preferably without underscores. - - classes should use the CapWords convention.
\ No newline at end of file diff --git a/cython/README b/cython/README deleted file mode 100644 index 7d2c4491..00000000 --- a/cython/README +++ /dev/null @@ -1,3 +0,0 @@ - -If you do not want to install the package, just launch the following command to help Python to find the compiled package : -$> export PYTHONPATH=`pwd`:$PYTHONPATH diff --git a/cython/cython/alpha_complex.pyx b/cython/cython/alpha_complex.pyx deleted file mode 100644 index 4f772e31..00000000 --- a/cython/cython/alpha_complex.pyx +++ /dev/null @@ -1,121 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Alpha_complex_interface.h" namespace "Gudhi": - cdef cppclass Alpha_complex_interface "Gudhi::alpha_complex::Alpha_complex_interface": - Alpha_complex_interface(vector[vector[double]] points) - # bool from_file is a workaround for cython to find the correct signature - Alpha_complex_interface(string off_file, bool from_file) - vector[double] get_point(int vertex) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) - -# AlphaComplex python interface -cdef class AlphaComplex: - """AlphaComplex is a simplicial complex constructed from the finite cells - of a Delaunay Triangulation. - - The filtration value of each simplex is computed as the square of the - circumradius of the simplex if the circumsphere is empty (the simplex is - then said to be Gabriel), and as the minimum of the filtration values of - the codimension 1 cofaces that make it not Gabriel otherwise. - - All simplices that have a filtration value strictly greater than a given - alpha squared value are not inserted into the complex. - - .. note:: - - When Alpha_complex is constructed with an infinite value of alpha, the - complex is a Delaunay complex. - - """ - - cdef Alpha_complex_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, points=None, off_file=''): - """AlphaComplex constructor. - - :param points: A list of points in d-Dimension. - :type points: list of list of double - - Or - - :param off_file: An OFF file style name. - :type off_file: string - """ - - # The real cython constructor - def __cinit__(self, points=None, off_file=''): - if off_file is not '': - if os.path.isfile(off_file): - self.thisptr = new Alpha_complex_interface(str.encode(off_file), True) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty Alpha construction - points=[] - self.thisptr = new Alpha_complex_interface(points) - - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if AlphaComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def get_point(self, vertex): - """This function returns the point corresponding to a given vertex. - - :param vertex: The vertex. - :type vertex: int - :rtype: list of float - :returns: the point. - """ - cdef vector[double] point = self.thisptr.get_point(vertex) - return point - - def create_simplex_tree(self, max_alpha_square=float('inf')): - """ - :param max_alpha_square: The maximum alpha square threshold the - simplices shall not exceed. Default is set to infinity. - :type max_alpha_square: float - :returns: A simplex tree created from the Delaunay Triangulation. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square) - return simplex_tree diff --git a/cython/cython/bottleneck_distance.pyx b/cython/cython/bottleneck_distance.pyx deleted file mode 100644 index 76ef81f4..00000000 --- a/cython/cython/bottleneck_distance.pyx +++ /dev/null @@ -1,61 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Bottleneck_distance_interface.h" namespace "Gudhi::persistence_diagram": - double bottleneck(vector[pair[double, double]], vector[pair[double, double]], double) - double bottleneck(vector[pair[double, double]], vector[pair[double, double]]) - -def bottleneck_distance(diagram_1, diagram_2, e=None): - """This function returns the point corresponding to a given vertex. - - :param diagram_1: The first diagram. - :type diagram_1: vector[pair[double, double]] - :param diagram_2: The second diagram. - :type diagram_2: vector[pair[double, double]] - :param e: If `e` is 0, this uses an expensive algorithm to compute the - exact distance. - If `e` is not 0, it asks for an additive `e`-approximation, and - currently also allows a small multiplicative error (the last 2 or 3 - bits of the mantissa may be wrong). This version of the algorithm takes - advantage of the limited precision of `double` and is usually a lot - faster to compute, whatever the value of `e`. - - Thus, by default, `e` is the smallest positive double. - :type e: float - :rtype: float - :returns: the bottleneck distance. - """ - if e is None: - # Default value is the smallest double value (not 0, 0 is for exact version) - return bottleneck(diagram_1, diagram_2) - else: - # Can be 0 for exact version - return bottleneck(diagram_1, diagram_2, e) diff --git a/cython/cython/cubical_complex.pyx b/cython/cython/cubical_complex.pyx deleted file mode 100644 index e94cd539..00000000 --- a/cython/cython/cubical_complex.pyx +++ /dev/null @@ -1,198 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Cubical_complex_interface.h" namespace "Gudhi": - cdef cppclass Bitmap_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<>": - Bitmap_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells) - Bitmap_cubical_complex_base_interface(string perseus_file) - int num_simplices() - int dimension() - -cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": - cdef cppclass Cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<>>": - Cubical_complex_persistence_interface(Bitmap_cubical_complex_base_interface * st, bool persistence_dim_max) - vector[pair[int, pair[double, double]]] get_persistence(int homology_coeff_field, double min_persistence) - vector[int] betti_numbers() - vector[int] persistent_betti_numbers(double from_value, double to_value) - vector[pair[double,double]] intervals_in_dimension(int dimension) - -# CubicalComplex python interface -cdef class CubicalComplex: - """The CubicalComplex is an example of a structured complex useful in - computational mathematics (specially rigorous numerics) and image - analysis. - """ - cdef Bitmap_cubical_complex_base_interface * thisptr - - cdef Cubical_complex_persistence_interface * pcohptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, dimensions=None, top_dimensional_cells=None, - perseus_file=''): - """CubicalComplex constructor from dimensions and - top_dimensional_cells or from a Perseus-style file name. - - :param dimensions: A list of number of top dimensional cells. - :type dimensions: list of int - :param top_dimensional_cells: A list of cells filtration values. - :type top_dimensional_cells: list of double - - Or - - :param perseus_file: A Perseus-style file name. - :type perseus_file: string - """ - - # The real cython constructor - def __cinit__(self, dimensions=None, top_dimensional_cells=None, - perseus_file=''): - if (dimensions is not None) and (top_dimensional_cells is not None) and (perseus_file is ''): - self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells) - elif (dimensions is None) and (top_dimensional_cells is None) and (perseus_file is not ''): - if os.path.isfile(perseus_file): - self.thisptr = new Bitmap_cubical_complex_base_interface(str.encode(perseus_file)) - else: - print("file " + perseus_file + " not found.") - else: - print("CubicalComplex can be constructed from dimensions and " - "top_dimensional_cells or from a Perseus-style file name.") - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - if self.pcohptr != NULL: - del self.pcohptr - - def __is_defined(self): - """Returns true if CubicalComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def __is_persistence_defined(self): - """Returns true if Persistence pointer is not NULL. - """ - return self.pcohptr != NULL - - def num_simplices(self): - """This function returns the number of all cubes in the complex. - - :returns: int -- the number of all cubes in the complex. - """ - return self.thisptr.num_simplices() - - def dimension(self): - """This function returns the dimension of the complex. - - :returns: int -- the complex dimension. - """ - return self.thisptr.dimension() - - def persistence(self, homology_coeff_field=11, min_persistence=0): - """This function returns the persistence of the complex. - - :param homology_coeff_field: The homology coefficient field. Must be a - prime number - :type homology_coeff_field: int. - :param min_persistence: The minimum persistence value to take into - account (strictly greater than min_persistence). Default value is - 0.0. - Sets min_persistence to -1.0 to see all values. - :type min_persistence: float. - :returns: list of pairs(dimension, pair(birth, death)) -- the - persistence of the complex. - """ - if self.pcohptr != NULL: - del self.pcohptr - if self.thisptr != NULL: - self.pcohptr = new Cubical_complex_persistence_interface(self.thisptr, True) - cdef vector[pair[int, pair[double, double]]] persistence_result - if self.pcohptr != NULL: - persistence_result = self.pcohptr.get_persistence(homology_coeff_field, min_persistence) - return persistence_result - - def betti_numbers(self): - """This function returns the Betti numbers of the complex. - - :returns: list of int -- The Betti numbers ([B0, B1, ..., Bn]). - - :note: betti_numbers function requires persistence function to be - launched first. - - :note: betti_numbers function always returns [1, 0, 0, ...] as infinity - filtration cubes are not removed from the complex. - """ - cdef vector[int] bn_result - if self.pcohptr != NULL: - bn_result = self.pcohptr.betti_numbers() - return bn_result - - def persistent_betti_numbers(self, from_value, to_value): - """This function returns the persistent Betti numbers of the complex. - - :param from_value: The persistence birth limit to be added in the - numbers (persistent birth <= from_value). - :type from_value: float. - :param to_value: The persistence death limit to be added in the - numbers (persistent death > to_value). - :type to_value: float. - - :returns: list of int -- The persistent Betti numbers ([B0, B1, ..., - Bn]). - - :note: persistent_betti_numbers function requires persistence - function to be launched first. - """ - cdef vector[int] pbn_result - if self.pcohptr != NULL: - pbn_result = self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) - return pbn_result - - def persistence_intervals_in_dimension(self, dimension): - """This function returns the persistence intervals of the complex in a - specific dimension. - - :param dimension: The specific dimension. - :type from_value: int. - :returns: The persistence intervals. - :rtype: list of pair of float - - :note: intervals_in_dim function requires persistence function to be - launched first. - """ - cdef vector[pair[double,double]] intervals_result - if self.pcohptr != NULL: - intervals_result = self.pcohptr.intervals_in_dimension(dimension) - else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") - return intervals_result diff --git a/cython/cython/euclidean_strong_witness_complex.pyx b/cython/cython/euclidean_strong_witness_complex.pyx deleted file mode 100644 index 62b7cf71..00000000 --- a/cython/cython/euclidean_strong_witness_complex.pyx +++ /dev/null @@ -1,97 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Euclidean_strong_witness_complex_interface.h" namespace "Gudhi": - cdef cppclass Euclidean_strong_witness_complex_interface "Gudhi::witness_complex::Euclidean_strong_witness_complex_interface": - Euclidean_strong_witness_complex_interface(vector[vector[double]] landmarks, vector[vector[double]] witnesses) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) - vector[double] get_point(unsigned vertex) - -# EuclideanStrongWitnessComplex python interface -cdef class EuclideanStrongWitnessComplex: - """Constructs strong witness complex for given sets of witnesses and - landmarks in Euclidean space. - """ - - cdef Euclidean_strong_witness_complex_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, landmarks=None, witnesses=None): - """WitnessComplex constructor. - - :param landmarks: A list of landmarks (in the point cloud). - :type landmarks: list of list of double - - :param witnesses: The point cloud. - :type witnesses: list of list of double - """ - - # The real cython constructor - def __cinit__(self, landmarks=None, witnesses=None): - if landmarks is not None and witnesses is not None: - self.thisptr = new Euclidean_strong_witness_complex_interface(landmarks, witnesses) - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if WitnessComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def create_simplex_tree(self, max_alpha_square, limit_dimension = -1): - """ - :param max_alpha_square: The maximum alpha square threshold the - simplices shall not exceed. Default is set to infinity. - :type max_alpha_square: float - :returns: A simplex tree created from the Delaunay Triangulation. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - if limit_dimension is not -1: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square, limit_dimension) - else: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square) - return simplex_tree - - def get_point(self, vertex): - """This function returns the point corresponding to a given vertex. - - :param vertex: The vertex. - :type vertex: int. - :returns: The point. - :rtype: list of float - """ - cdef vector[double] point = self.thisptr.get_point(vertex) - return point - diff --git a/cython/cython/euclidean_witness_complex.pyx b/cython/cython/euclidean_witness_complex.pyx deleted file mode 100644 index c10ca73d..00000000 --- a/cython/cython/euclidean_witness_complex.pyx +++ /dev/null @@ -1,97 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Euclidean_witness_complex_interface.h" namespace "Gudhi": - cdef cppclass Euclidean_witness_complex_interface "Gudhi::witness_complex::Euclidean_witness_complex_interface": - Euclidean_witness_complex_interface(vector[vector[double]] landmarks, vector[vector[double]] witnesses) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) - vector[double] get_point(unsigned vertex) - -# EuclideanWitnessComplex python interface -cdef class EuclideanWitnessComplex: - """Constructs (weak) witness complex for given sets of witnesses and - landmarks in Euclidean space. - """ - - cdef Euclidean_witness_complex_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, landmarks=None, witnesses=None): - """WitnessComplex constructor. - - :param landmarks: A list of landmarks (in the point cloud). - :type landmarks: list of list of double - - :param witnesses: The point cloud. - :type witnesses: list of list of double - """ - - # The real cython constructor - def __cinit__(self, landmarks=None, witnesses=None): - if landmarks is not None and witnesses is not None: - self.thisptr = new Euclidean_witness_complex_interface(landmarks, witnesses) - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if WitnessComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def create_simplex_tree(self, max_alpha_square, limit_dimension = -1): - """ - :param max_alpha_square: The maximum alpha square threshold the - simplices shall not exceed. Default is set to infinity. - :type max_alpha_square: float - :returns: A simplex tree created from the Delaunay Triangulation. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - if limit_dimension is not -1: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square, limit_dimension) - else: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square) - return simplex_tree - - def get_point(self, vertex): - """This function returns the point corresponding to a given vertex. - - :param vertex: The vertex. - :type vertex: int. - :returns: The point. - :rtype: list of float - """ - cdef vector[double] point = self.thisptr.get_point(vertex) - return point - diff --git a/cython/cython/nerve_gic.pyx b/cython/cython/nerve_gic.pyx deleted file mode 100644 index 30a14d3b..00000000 --- a/cython/cython/nerve_gic.pyx +++ /dev/null @@ -1,401 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2018 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2018 Inria" -__license__ = "GPL v3" - -cdef extern from "Nerve_gic_interface.h" namespace "Gudhi": - cdef cppclass Nerve_gic_interface "Gudhi::cover_complex::Nerve_gic_interface": - Nerve_gic_interface() - double compute_confidence_level_from_distance(double distance) - double compute_distance_from_confidence_level(double alpha) - void compute_distribution(int N) - double compute_p_value() - void compute_PD() - void find_simplices() - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree) - bool read_point_cloud(string off_file_name) - double set_automatic_resolution() - void set_color_from_coordinate(int k) - void set_color_from_file(string color_file_name) - void set_color_from_vector(vector[double] color) - void set_cover_from_file(string cover_file_name) - void set_cover_from_function() - void set_cover_from_Euclidean_Voronoi(int m) - void set_function_from_coordinate(int k) - void set_function_from_file(string func_file_name) - void set_function_from_range(vector[double] function) - void set_gain(double g) - double set_graph_from_automatic_euclidean_rips(int N) - void set_graph_from_file(string graph_file_name) - void set_graph_from_OFF() - void set_graph_from_euclidean_rips(double threshold) - void set_mask(int nodemask) - void set_resolution_with_interval_length(double resolution) - void set_resolution_with_interval_number(int resolution) - void set_subsampling(double constant, double power) - void set_type(string type) - void set_verbose(bool verbose) - vector[int] subpopulation(int c) - void write_info() - void plot_DOT() - void plot_OFF() - -# CoverComplex python interface -cdef class CoverComplex: - """Cover complex data structure. - - The data structure is a simplicial complex, representing a Graph Induced - simplicial Complex (GIC) or a Nerve, and whose simplices are computed with - a cover C of a point cloud P, which often comes from the preimages of - intervals covering the image of a function f defined on P. These intervals - are parameterized by their resolution (either their length or their number) - and their gain (percentage of overlap). To compute a GIC, one also needs a - graph G built on top of P, whose cliques with vertices belonging to - different elements of C correspond to the simplices of the GIC. - """ - - cdef Nerve_gic_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self): - """CoverComplex constructor. - """ - - # The real cython constructor - def __cinit__(self): - self.thisptr = new Nerve_gic_interface() - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if CoverComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def compute_confidence_level_from_distance(self, distance): - """Computes the confidence level of a specific bottleneck distance - threshold. - - :param distance: Bottleneck distance. - :type distance: double - :rtype: double - :returns: Confidence level. - """ - return self.thisptr.compute_confidence_level_from_distance(distance) - - def compute_distance_from_confidence_level(self, alpha): - """Computes the bottleneck distance threshold corresponding to a - specific confidence level. - - :param alpha: Confidence level. - :type alpha: double - :rtype: double - :returns: Bottleneck distance. - """ - return self.thisptr.compute_distance_from_confidence_level(alpha) - - def compute_distribution(self, N=100): - """Computes bootstrapped distances distribution. - - :param N: Loop number (default value is 100). - :type alpha: int - """ - self.thisptr.compute_distribution(N) - - def compute_p_value(self): - """Computes the p-value, i.e. the opposite of the confidence level of - the largest bottleneck distance preserving the points in the - persistence diagram of the output simplicial complex. - - :rtype: double - :returns: p-value. - """ - return self.thisptr.compute_p_value() - - def compute_PD(self): - """Computes the extended persistence diagram of the complex. - """ - self.thisptr.compute_PD() - - def create_simplex_tree(self): - """ - :returns: A simplex tree created from the Cover complex. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - self.thisptr.create_simplex_tree(simplex_tree.thisptr) - return simplex_tree - - def find_simplices(self): - """Computes the simplices of the simplicial complex. - """ - self.thisptr.find_simplices() - - def read_point_cloud(self, off_file): - """Reads and stores the input point cloud. - - :param off_file: Name of the input .OFF or .nOFF file. - :type off_file: string - :rtype: bool - :returns: Read file status. - """ - if os.path.isfile(off_file): - return self.thisptr.read_point_cloud(str.encode(off_file)) - else: - print("file " + off_file + " not found.") - return False - - def set_automatic_resolution(self): - """Computes the optimal length of intervals (i.e. the smallest interval - length avoiding discretization artifacts—see :cite:`Carriere17c`) for a - functional cover. - - :rtype: double - :returns: reso interval length used to compute the cover. - """ - return self.thisptr.set_automatic_resolution() - - def set_color_from_coordinate(self, k=0): - """Computes the function used to color the nodes of the simplicial - complex from the k-th coordinate. - - :param k: Coordinate to use (start at 0). Default value is 0. - :type k: int - """ - return self.thisptr.set_color_from_coordinate(k) - - def set_color_from_file(self, color_file_name): - """Computes the function used to color the nodes of the simplicial - complex from a file containing the function values. - - :param color_file_name: Name of the input color file. - :type color_file_name: string - """ - if os.path.isfile(color_file_name): - self.thisptr.set_color_from_file(str.encode(color_file_name)) - else: - print("file " + color_file_name + " not found.") - - def set_color_from_vector(self, color): - """Computes the function used to color the nodes of the simplicial - complex from a vector stored in memory. - - :param color: Input vector of values. - :type color: vector[double] - """ - self.thisptr.set_color_from_vector(color) - - def set_cover_from_file(self, cover_file_name): - """Creates the cover C from a file containing the cover elements of - each point (the order has to be the same as in the input file!). - - :param cover_file_name: Name of the input cover file. - :type cover_file_name: string - """ - if os.path.isfile(cover_file_name): - self.thisptr.set_cover_from_file(str.encode(cover_file_name)) - else: - print("file " + cover_file_name + " not found.") - - def set_cover_from_function(self): - """Creates a cover C from the preimages of the function f. - """ - self.thisptr.set_cover_from_function() - - def set_cover_from_Voronoi(self, m=100): - """Creates the cover C from the Voronoï cells of a subsampling of the - point cloud. - - :param m: Number of points in the subsample. Default value is 100. - :type m: int - """ - self.thisptr.set_cover_from_Euclidean_Voronoi(m) - - def set_function_from_coordinate(self, k): - """Creates the function f from the k-th coordinate of the point cloud. - - :param k: Coordinate to use (start at 0). - :type k: int - """ - self.thisptr.set_function_from_coordinate(k) - - def set_function_from_file(self, func_file_name): - """Creates the function f from a file containing the function values. - - :param func_file_name: Name of the input function file. - :type func_file_name: string - """ - if os.path.isfile(func_file_name): - self.thisptr.set_function_from_file(str.encode(func_file_name)) - else: - print("file " + func_file_name + " not found.") - - def set_function_from_range(self, function): - """Creates the function f from a vector stored in memory. - - :param function: Input vector of values. - :type function: vector[double] - """ - self.thisptr.set_function_from_range(function) - - def set_gain(self, g = 0.3): - """Sets a gain from a value stored in memory. - - :param g: Gain (default value is 0.3). - :type g: double - """ - self.thisptr.set_gain(g) - - def set_graph_from_automatic_rips(self, N=100): - """Creates a graph G from a Rips complex whose threshold value is - automatically tuned with subsampling—see. - - :param N: Number of subsampling iteration (the default reasonable value - is 100, but there is no guarantee on how to choose it). - :type N: int - :rtype: double - :returns: Delta threshold used for computing the Rips complex. - """ - return self.thisptr.set_graph_from_automatic_euclidean_rips(N) - - def set_graph_from_file(self, graph_file_name): - """Creates a graph G from a file containing the edges. - - :param graph_file_name: Name of the input graph file. The graph file - contains one edge per line, each edge being represented by the IDs of - its two nodes. - :type graph_file_name: string - """ - if os.path.isfile(graph_file_name): - self.thisptr.set_graph_from_file(str.encode(graph_file_name)) - else: - print("file " + graph_file_name + " not found.") - - def set_graph_from_OFF(self): - """Creates a graph G from the triangulation given by the input OFF - file. - """ - self.thisptr.set_graph_from_OFF() - - def set_graph_from_rips(self, threshold): - """Creates a graph G from a Rips complex. - - :param threshold: Threshold value for the Rips complex. - :type threshold: double - """ - self.thisptr.set_graph_from_euclidean_rips(threshold) - - def set_mask(self, nodemask): - """Sets the mask, which is a threshold integer such that nodes in the - complex that contain a number of data points which is less than or - equal to this threshold are not displayed. - - :param nodemask: Threshold. - :type nodemask: int - """ - self.thisptr.set_mask(nodemask) - - def set_resolution_with_interval_length(self, resolution): - """Sets a length of intervals from a value stored in memory. - - :param resolution: Length of intervals. - :type resolution: double - """ - self.thisptr.set_resolution_with_interval_length(resolution) - - def set_resolution_with_interval_number(self, resolution): - """Sets a number of intervals from a value stored in memory. - - :param resolution: Number of intervals. - :type resolution: int - """ - self.thisptr.set_resolution_with_interval_number(resolution) - - def set_subsampling(self, constant, power): - """Sets the constants used to subsample the data set. These constants - are explained in :cite:`Carriere17c`. - - :param constant: Constant. - :type constant: double - :param power: Power. - :type resolution: double - """ - self.thisptr.set_subsampling(constant, power) - - def set_type(self, type): - """Specifies whether the type of the output simplicial complex. - - :param type: either "GIC" or "Nerve". - :type type: string - """ - self.thisptr.set_type(str.encode(type)) - - def set_verbose(self, verbose): - """Specifies whether the program should display information or not. - - :param verbose: true = display info, false = do not display info. - :type verbose: boolean - """ - self.thisptr.set_verbose(verbose) - - def subpopulation(self, c): - """Returns the data subset corresponding to a specific node of the - created complex. - - :param c: ID of the node. - :type c: int - :rtype: vector[int] - :returns: Vector of IDs of data points. - """ - return self.thisptr.subpopulation(c) - - def write_info(self): - """Creates a .txt file called SC.txt describing the 1-skeleton, which can - then be plotted with e.g. KeplerMapper. - """ - return self.thisptr.write_info() - - def plot_dot(self): - """Creates a .dot file called SC.dot for neato (part of the graphviz - package) once the simplicial complex is computed to get a visualization of - its 1-skeleton in a .pdf file. - """ - return self.thisptr.plot_DOT() - - def plot_off(self): - """Creates a .off file called SC.off for 3D visualization, which contains - the 2-skeleton of the GIC. This function assumes that the cover has been - computed with Voronoi. If data points are in 1D or 2D, the remaining - coordinates of the points embedded in 3D are set to 0. - """ - return self.thisptr.plot_OFF() diff --git a/cython/cython/off_reader.pyx b/cython/cython/off_reader.pyx deleted file mode 100644 index b939013f..00000000 --- a/cython/cython/off_reader.pyx +++ /dev/null @@ -1,50 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.string cimport string -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Off_reader_interface.h" namespace "Gudhi": - vector[vector[double]] read_points_from_OFF_file(string off_file) - -def read_off(off_file=''): - """Read points from OFF file. - - :param off_file: An OFF file style name. - :type off_file: string - - :returns: The point set. - :rtype: vector[vector[double]] - """ - if off_file is not '': - if os.path.isfile(off_file): - return read_points_from_OFF_file(str.encode(off_file)) - else: - print("file " + off_file + " not found.") - return [] - diff --git a/cython/cython/periodic_cubical_complex.pyx b/cython/cython/periodic_cubical_complex.pyx deleted file mode 100644 index e626950b..00000000 --- a/cython/cython/periodic_cubical_complex.pyx +++ /dev/null @@ -1,200 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Cubical_complex_interface.h" namespace "Gudhi": - cdef cppclass Periodic_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>": - Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions) - Periodic_cubical_complex_base_interface(string perseus_file) - int num_simplices() - int dimension() - -cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": - cdef cppclass Periodic_cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>>": - Periodic_cubical_complex_persistence_interface(Periodic_cubical_complex_base_interface * st, bool persistence_dim_max) - vector[pair[int, pair[double, double]]] get_persistence(int homology_coeff_field, double min_persistence) - vector[int] betti_numbers() - vector[int] persistent_betti_numbers(double from_value, double to_value) - vector[pair[double,double]] intervals_in_dimension(int dimension) - -# PeriodicCubicalComplex python interface -cdef class PeriodicCubicalComplex: - """The PeriodicCubicalComplex is an example of a structured complex useful - in computational mathematics (specially rigorous numerics) and image - analysis. - """ - cdef Periodic_cubical_complex_base_interface * thisptr - - cdef Periodic_cubical_complex_persistence_interface * pcohptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, dimensions=None, top_dimensional_cells=None, - periodic_dimensions=None, perseus_file=''): - """PeriodicCubicalComplex constructor from dimensions and - top_dimensional_cells or from a Perseus-style file name. - - :param dimensions: A list of number of top dimensional cells. - :type dimensions: list of int - :param top_dimensional_cells: A list of cells filtration values. - :type top_dimensional_cells: list of double - :param periodic_dimensions: A list of top dimensional cells periodicity value. - :type periodic_dimensions: list of boolean - - Or - - :param perseus_file: A Perseus-style file name. - :type perseus_file: string - """ - - # The real cython constructor - def __cinit__(self, dimensions=None, top_dimensional_cells=None, - periodic_dimensions=None, perseus_file=''): - if (dimensions is not None) and (top_dimensional_cells is not None) and (periodic_dimensions is not None) and (perseus_file is ''): - self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, top_dimensional_cells, periodic_dimensions) - elif (dimensions is None) and (top_dimensional_cells is None) and (periodic_dimensions is None) and (perseus_file is not ''): - if os.path.isfile(perseus_file): - self.thisptr = new Periodic_cubical_complex_base_interface(str.encode(perseus_file)) - else: - print("file " + perseus_file + " not found.") - else: - print("CubicalComplex can be constructed from dimensions and " - "top_dimensional_cells or from a Perseus-style file name.") - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - if self.pcohptr != NULL: - del self.pcohptr - - def __is_defined(self): - """Returns true if PeriodicCubicalComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def __is_persistence_defined(self): - """Returns true if Persistence pointer is not NULL. - """ - return self.pcohptr != NULL - - def num_simplices(self): - """This function returns the number of all cubes in the complex. - - :returns: int -- the number of all cubes in the complex. - """ - return self.thisptr.num_simplices() - - def dimension(self): - """This function returns the dimension of the complex. - - :returns: int -- the complex dimension. - """ - return self.thisptr.dimension() - - def persistence(self, homology_coeff_field=11, min_persistence=0): - """This function returns the persistence of the complex. - - :param homology_coeff_field: The homology coefficient field. Must be a - prime number - :type homology_coeff_field: int. - :param min_persistence: The minimum persistence value to take into - account (strictly greater than min_persistence). Default value is - 0.0. - Sets min_persistence to -1.0 to see all values. - :type min_persistence: float. - :returns: list of pairs(dimension, pair(birth, death)) -- the - persistence of the complex. - """ - if self.pcohptr != NULL: - del self.pcohptr - if self.thisptr != NULL: - self.pcohptr = new Periodic_cubical_complex_persistence_interface(self.thisptr, True) - cdef vector[pair[int, pair[double, double]]] persistence_result - if self.pcohptr != NULL: - persistence_result = self.pcohptr.get_persistence(homology_coeff_field, min_persistence) - return persistence_result - - def betti_numbers(self): - """This function returns the Betti numbers of the complex. - - :returns: list of int -- The Betti numbers ([B0, B1, ..., Bn]). - - :note: betti_numbers function requires persistence function to be - launched first. - - :note: betti_numbers function always returns [1, 0, 0, ...] as infinity - filtration cubes are not removed from the complex. - """ - cdef vector[int] bn_result - if self.pcohptr != NULL: - bn_result = self.pcohptr.betti_numbers() - return bn_result - - def persistent_betti_numbers(self, from_value, to_value): - """This function returns the persistent Betti numbers of the complex. - - :param from_value: The persistence birth limit to be added in the - numbers (persistent birth <= from_value). - :type from_value: float. - :param to_value: The persistence death limit to be added in the - numbers (persistent death > to_value). - :type to_value: float. - - :returns: list of int -- The persistent Betti numbers ([B0, B1, ..., - Bn]). - - :note: persistent_betti_numbers function requires persistence - function to be launched first. - """ - cdef vector[int] pbn_result - if self.pcohptr != NULL: - pbn_result = self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) - return pbn_result - - def persistence_intervals_in_dimension(self, dimension): - """This function returns the persistence intervals of the complex in a - specific dimension. - - :param dimension: The specific dimension. - :type from_value: int. - :returns: The persistence intervals. - :rtype: list of pair of float - - :note: intervals_in_dim function requires persistence function to be - launched first. - """ - cdef vector[pair[double,double]] intervals_result - if self.pcohptr != NULL: - intervals_result = self.pcohptr.intervals_in_dimension(dimension) - else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") - return intervals_result diff --git a/cython/cython/persistence_graphical_tools.py b/cython/cython/persistence_graphical_tools.py deleted file mode 100755 index 314bd6db..00000000 --- a/cython/cython/persistence_graphical_tools.py +++ /dev/null @@ -1,220 +0,0 @@ -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau, Bertrand Michel - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau, Bertrand Michel" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -try: - import matplotlib.pyplot as plt - import matplotlib.patches as mpatches - import numpy as np - import os - - def __min_birth_max_death(persistence, band=0.): - """This function returns (min_birth, max_death) from the persistence. - - :param persistence: The persistence to plot. - :type persistence: list of tuples(dimension, tuple(birth, death)). - :param band: band - :type band: float. - :returns: (float, float) -- (min_birth, max_death). - """ - # Look for minimum birth date and maximum death date for plot optimisation - max_death = 0 - min_birth = persistence[0][1][0] - for interval in reversed(persistence): - if float(interval[1][1]) != float('inf'): - if float(interval[1][1]) > max_death: - max_death = float(interval[1][1]) - if float(interval[1][0]) > max_death: - max_death = float(interval[1][0]) - if float(interval[1][0]) < min_birth: - min_birth = float(interval[1][0]) - if band > 0.: - max_death += band - return (min_birth, max_death) - - """ - Only 13 colors for the palette - """ - palette = ['#ff0000', '#00ff00', '#0000ff', '#00ffff', '#ff00ff', '#ffff00', - '#000000', '#880000', '#008800', '#000088', '#888800', '#880088', - '#008888'] - - def plot_persistence_barcode(persistence=[], persistence_file='', alpha=0.6, - max_barcodes=1000, inf_delta=0.1, legend=False): - """This function plots the persistence bar code from persistence values list - or from a :doc:`persistence file <fileformats>`. - - :param persistence: Persistence values list. - :type persistence: list of tuples(dimension, tuple(birth, death)). - :param persistence_file: A :doc:`persistence file <fileformats>` style name - (reset persistence if both are set). - :type persistence_file: string - :param alpha: barcode transparency value (0.0 transparent through 1.0 opaque - default is 0.6). - :type alpha: float. - :param max_barcodes: number of maximal barcodes to be displayed. - Set it to 0 to see all, Default value is 1000. - (persistence will be sorted by life time if max_barcodes is set) - :type max_barcodes: int. - :param inf_delta: Infinity is placed at ((max_death - min_birth) x inf_delta). - A reasonable value is between 0.05 and 0.5 - default is 0.1. - :type inf_delta: float. - :returns: A matplotlib object containing horizontal bar plot of persistence - (launch `show()` method on it to display it). - """ - if persistence_file is not '': - if os.path.isfile(persistence_file): - # Reset persistence - persistence = [] - diag = read_persistence_intervals_grouped_by_dimension(persistence_file=persistence_file) - for key in diag.keys(): - for persistence_interval in diag[key]: - persistence.append((key, persistence_interval)) - else: - print("file " + persistence_file + " not found.") - return None - - if max_barcodes > 0 and max_barcodes < len(persistence): - # Sort by life time, then takes only the max_plots elements - persistence = sorted(persistence, key=lambda life_time: life_time[1][1]-life_time[1][0], reverse=True)[:max_barcodes] - - persistence = sorted(persistence, key=lambda birth: birth[1][0]) - - (min_birth, max_death) = __min_birth_max_death(persistence) - ind = 0 - delta = ((max_death - min_birth) * inf_delta) - # Replace infinity values with max_death + delta for bar code to be more - # readable - infinity = max_death + delta - axis_start = min_birth - delta - # Draw horizontal bars in loop - for interval in reversed(persistence): - if float(interval[1][1]) != float('inf'): - # Finite death case - plt.barh(ind, (interval[1][1] - interval[1][0]), height=0.8, - left = interval[1][0], alpha=alpha, - color = palette[interval[0]], - linewidth=0) - else: - # Infinite death case for diagram to be nicer - plt.barh(ind, (infinity - interval[1][0]), height=0.8, - left = interval[1][0], alpha=alpha, - color = palette[interval[0]], - linewidth=0) - ind = ind + 1 - - if legend: - dimensions = list(set(item[0] for item in persistence)) - plt.legend(handles=[mpatches.Patch(color=palette[dim], - label=str(dim)) for dim in dimensions], - loc='lower right') - plt.title('Persistence barcode') - # Ends plot on infinity value and starts a little bit before min_birth - plt.axis([axis_start, infinity, 0, ind]) - return plt - - def plot_persistence_diagram(persistence=[], persistence_file='', alpha=0.6, - band=0., max_plots=1000, inf_delta=0.1, legend=False): - """This function plots the persistence diagram from persistence values list - or from a :doc:`persistence file <fileformats>`. - - :param persistence: Persistence values list. - :type persistence: list of tuples(dimension, tuple(birth, death)). - :param persistence_file: A :doc:`persistence file <fileformats>` style name - (reset persistence if both are set). - :type persistence_file: string - :param alpha: plot transparency value (0.0 transparent through 1.0 opaque - default is 0.6). - :type alpha: float. - :param band: band (not displayed if :math:`\leq` 0. - default is 0.) - :type band: float. - :param max_plots: number of maximal plots to be displayed - Set it to 0 to see all, Default value is 1000. - (persistence will be sorted by life time if max_plots is set) - :type max_plots: int. - :param inf_delta: Infinity is placed at ((max_death - min_birth) x inf_delta). - A reasonable value is between 0.05 and 0.5 - default is 0.1. - :type inf_delta: float. - :returns: A matplotlib object containing diagram plot of persistence - (launch `show()` method on it to display it). - """ - if persistence_file is not '': - if os.path.isfile(persistence_file): - # Reset persistence - persistence = [] - diag = read_persistence_intervals_grouped_by_dimension(persistence_file=persistence_file) - for key in diag.keys(): - for persistence_interval in diag[key]: - persistence.append((key, persistence_interval)) - else: - print("file " + persistence_file + " not found.") - return None - - if max_plots > 0 and max_plots < len(persistence): - # Sort by life time, then takes only the max_plots elements - persistence = sorted(persistence, key=lambda life_time: life_time[1][1]-life_time[1][0], reverse=True)[:max_plots] - - (min_birth, max_death) = __min_birth_max_death(persistence, band) - ind = 0 - delta = ((max_death - min_birth) * inf_delta) - # Replace infinity values with max_death + delta for diagram to be more - # readable - infinity = max_death + delta - axis_start = min_birth - delta - - # line display of equation : birth = death - x = np.linspace(axis_start, infinity, 1000) - # infinity line and text - plt.plot(x, x, color='k', linewidth=1.0) - plt.plot(x, [infinity] * len(x), linewidth=1.0, color='k', alpha=alpha) - plt.text(axis_start, infinity, r'$\infty$', color='k', alpha=alpha) - # bootstrap band - if band > 0.: - plt.fill_between(x, x, x+band, alpha=alpha, facecolor='red') - - # Draw points in loop - for interval in reversed(persistence): - if float(interval[1][1]) != float('inf'): - # Finite death case - plt.scatter(interval[1][0], interval[1][1], alpha=alpha, - color = palette[interval[0]]) - else: - # Infinite death case for diagram to be nicer - plt.scatter(interval[1][0], infinity, alpha=alpha, - color = palette[interval[0]]) - ind = ind + 1 - - if legend: - dimensions = list(set(item[0] for item in persistence)) - plt.legend(handles=[mpatches.Patch(color=palette[dim], label=str(dim)) for dim in dimensions]) - - plt.title('Persistence diagram') - plt.xlabel('Birth') - plt.ylabel('Death') - # Ends plot on infinity value and starts a little bit before min_birth - plt.axis([axis_start, infinity, axis_start, infinity + delta]) - return plt - -except ImportError: - # Continue in case of import error, functions won't be available - pass diff --git a/cython/cython/reader_utils.pyx b/cython/cython/reader_utils.pyx deleted file mode 100644 index e4572db0..00000000 --- a/cython/cython/reader_utils.pyx +++ /dev/null @@ -1,95 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.string cimport string -from libcpp.map cimport map -from libcpp.pair cimport pair -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2017 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2017 Inria" -__license__ = "GPL v3" - -cdef extern from "Reader_utils_interface.h" namespace "Gudhi": - vector[vector[double]] read_matrix_from_csv_file(string off_file, char separator) - map[int, vector[pair[double, double]]] read_pers_intervals_grouped_by_dimension(string filename) - vector[pair[double, double]] read_pers_intervals_in_dimension(string filename, int only_this_dim) - -def read_lower_triangular_matrix_from_csv_file(csv_file='', separator=';'): - """Read lower triangular matrix from a CSV style file. - - :param csv_file: A CSV file style name. - :type csv_file: string - :param separator: The value separator in the CSV file. Default value is ';' - :type separator: char - - :returns: The lower triangular matrix. - :rtype: vector[vector[double]] - """ - if csv_file is not '': - if os.path.isfile(csv_file): - return read_matrix_from_csv_file(str.encode(csv_file), ord(separator[0])) - print("file " + csv_file + " not set or not found.") - return [] - -def read_persistence_intervals_grouped_by_dimension(persistence_file=''): - """Reads a file containing persistence intervals. - Each line might contain 2, 3 or 4 values: [[field] dimension] birth death - The return value is an `map[dim, vector[pair[birth, death]]]` - where `dim` is an `int`, `birth` a `double`, and `death` a `double`. - Note: the function does not check that birth <= death. - - :param persistence_file: A persistence file style name. - :type persistence_file: string - - :returns: The persistence pairs grouped by dimension. - :rtype: map[int, vector[pair[double, double]]] - """ - if persistence_file is not '': - if os.path.isfile(persistence_file): - return read_pers_intervals_grouped_by_dimension(str.encode(persistence_file)) - print("file " + persistence_file + " not set or not found.") - return [] - -def read_persistence_intervals_in_dimension(persistence_file='', only_this_dim=-1): - """Reads a file containing persistence intervals. - Each line might contain 2, 3 or 4 values: [[field] dimension] birth death - If `only_this_dim` = -1, dimension is ignored and all lines are returned. - If `only_this_dim` is >= 0, only the lines where dimension = `only_this_dim` - (or where dimension is not specified) are returned. - The return value is an `vector[pair[birth, death]]` - where `birth` a `double`, and `death` a `double`. - Note: the function does not check that birth <= death. - - :param persistence_file: A persistence file style name. - :type persistence_file: string - - :returns: The persistence pairs grouped by dimension. - :rtype: map[int, vector[pair[double, double]]] - """ - if persistence_file is not '': - if os.path.isfile(persistence_file): - return read_pers_intervals_in_dimension(str.encode(persistence_file), only_this_dim) - print("file " + persistence_file + " not set or not found.") - return [] diff --git a/cython/cython/rips_complex.pyx b/cython/cython/rips_complex.pyx deleted file mode 100644 index 30ca4443..00000000 --- a/cython/cython/rips_complex.pyx +++ /dev/null @@ -1,96 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Rips_complex_interface.h" namespace "Gudhi": - cdef cppclass Rips_complex_interface "Gudhi::rips_complex::Rips_complex_interface": - Rips_complex_interface(vector[vector[double]] values, double threshold, bool euclidean) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, int dim_max) - -# RipsComplex python interface -cdef class RipsComplex: - """The data structure is a one skeleton graph, or Rips graph, containing - edges when the edge length is less or equal to a given threshold. Edge - length is computed from a user given point cloud with a given distance - function, or a distance matrix. - """ - - cdef Rips_complex_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, points=None, distance_matrix=None, max_edge_length=float('inf')): - """RipsComplex constructor. - - :param max_edge_length: Rips value. - :type max_edge_length: float - - :param points: A list of points in d-Dimension. - :type points: list of list of double - - Or - - :param distance_matrix: A distance matrix (full square or lower - triangular). - :type points: list of list of double - """ - - # The real cython constructor - def __cinit__(self, points=None, distance_matrix=None, max_edge_length=float('inf')): - if distance_matrix is not None: - self.thisptr = new Rips_complex_interface(distance_matrix, max_edge_length, False) - else: - if points is None: - # Empty Rips construction - points=[] - self.thisptr = new Rips_complex_interface(points, max_edge_length, True) - - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if RipsComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def create_simplex_tree(self, max_dimension=1): - """ - :param max_dimension: graph expansion for rips until this given maximal - dimension. - :type max_dimension: int - :returns: A simplex tree created from the Delaunay Triangulation. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_dimension) - return simplex_tree diff --git a/cython/cython/simplex_tree.pyx b/cython/cython/simplex_tree.pyx deleted file mode 100644 index e302486b..00000000 --- a/cython/cython/simplex_tree.pyx +++ /dev/null @@ -1,548 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp cimport bool -from libcpp.string cimport string - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": - cdef cppclass Simplex_tree_options_full_featured: - pass - - cdef cppclass Simplex_tree_interface_full_featured "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>": - Simplex_tree() - double simplex_filtration(vector[int] simplex) - void assign_simplex_filtration(vector[int] simplex, double filtration) - void initialize_filtration() - int num_vertices() - int num_simplices() - void set_dimension(int dimension) - int dimension() - int upper_bound_dimension() - bint find_simplex(vector[int] simplex) - bint insert_simplex_and_subfaces(vector[int] simplex, - double filtration) - vector[pair[vector[int], double]] get_filtration() - vector[pair[vector[int], double]] get_skeleton(int dimension) - vector[pair[vector[int], double]] get_star(vector[int] simplex) - vector[pair[vector[int], double]] get_cofaces(vector[int] simplex, - int dimension) - void expansion(int max_dim) - void remove_maximal_simplex(vector[int] simplex) - bool prune_above_filtration(double filtration) - bool make_filtration_non_decreasing() - -cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": - cdef cppclass Simplex_tree_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>": - Simplex_tree_persistence_interface(Simplex_tree_interface_full_featured * st, bool persistence_dim_max) - vector[pair[int, pair[double, double]]] get_persistence(int homology_coeff_field, double min_persistence) - vector[int] betti_numbers() - vector[int] persistent_betti_numbers(double from_value, double to_value) - vector[pair[double,double]] intervals_in_dimension(int dimension) - void write_output_diagram(string diagram_file_name) - vector[pair[vector[int], vector[int]]] persistence_pairs() - -# SimplexTree python interface -cdef class SimplexTree: - """The simplex tree is an efficient and flexible data structure for - representing general (filtered) simplicial complexes. The data structure - is described in Jean-Daniel Boissonnat and Clément Maria. The Simplex - Tree: An Efficient Data Structure for General Simplicial Complexes. - Algorithmica, pages 1–22, 2014. - - This class is a filtered, with keys, and non contiguous vertices version - of the simplex tree. - """ - cdef Simplex_tree_interface_full_featured * thisptr - - cdef Simplex_tree_persistence_interface * pcohptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self): - """SimplexTree constructor. - """ - - # The real cython constructor - def __cinit__(self): - self.thisptr = new Simplex_tree_interface_full_featured() - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - if self.pcohptr != NULL: - del self.pcohptr - - def __is_defined(self): - """Returns true if SimplexTree pointer is not NULL. - """ - return self.thisptr != NULL - - def __is_persistence_defined(self): - """Returns true if Persistence pointer is not NULL. - """ - return self.pcohptr != NULL - - def filtration(self, simplex): - """This function returns the filtration value for a given N-simplex in - this simplicial complex, or +infinity if it is not in the complex. - - :param simplex: The N-simplex, represented by a list of vertex. - :type simplex: list of int. - :returns: The simplicial complex filtration value. - :rtype: float - """ - return self.thisptr.simplex_filtration(simplex) - - def assign_filtration(self, simplex, filtration): - """This function assigns the simplicial complex filtration value for a - given N-simplex. - - :param simplex: The N-simplex, represented by a list of vertex. - :type simplex: list of int. - :param filtration: The simplicial complex filtration value. - :type filtration: float - """ - self.thisptr.assign_simplex_filtration(simplex, filtration) - - def initialize_filtration(self): - """This function initializes and sorts the simplicial complex - filtration vector. - - .. note:: - - This function must be launched before - :func:`persistence()<gudhi.SimplexTree.persistence>`, - :func:`betti_numbers()<gudhi.SimplexTree.betti_numbers>`, - :func:`persistent_betti_numbers()<gudhi.SimplexTree.persistent_betti_numbers>`, - or :func:`get_filtration()<gudhi.SimplexTree.get_filtration>` - after :func:`inserting<gudhi.SimplexTree.insert>` or - :func:`removing<gudhi.SimplexTree.remove_maximal_simplex>` - simplices. - """ - self.thisptr.initialize_filtration() - - def num_vertices(self): - """This function returns the number of vertices of the simplicial - complex. - - :returns: The simplicial complex number of vertices. - :rtype: int - """ - return self.thisptr.num_vertices() - - def num_simplices(self): - """This function returns the number of simplices of the simplicial - complex. - - :returns: the simplicial complex number of simplices. - :rtype: int - """ - return self.thisptr.num_simplices() - - def dimension(self): - """This function returns the dimension of the simplicial complex. - - :returns: the simplicial complex dimension. - :rtype: int - - .. note:: - - This function is not constant time because it can recompute - dimension if required (can be triggered by - :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` - or - :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` - methods). - """ - return self.thisptr.dimension() - - def upper_bound_dimension(self): - """This function returns a valid dimension upper bound of the - simplicial complex. - - :returns: an upper bound on the dimension of the simplicial complex. - :rtype: int - """ - return self.thisptr.upper_bound_dimension() - - def set_dimension(self, dimension): - """This function sets the dimension of the simplicial complex. - - :param dimension: The new dimension value. - :type dimension: int. - - .. note:: - - This function must be used with caution because it disables - dimension recomputation when required - (this recomputation can be triggered by - :func:`remove_maximal_simplex()<gudhi.SimplexTree.remove_maximal_simplex>` - or - :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` - ). - """ - self.thisptr.set_dimension(<int>dimension) - - def find(self, simplex): - """This function returns if the N-simplex was found in the simplicial - complex or not. - - :param simplex: The N-simplex to find, represented by a list of vertex. - :type simplex: list of int. - :returns: true if the simplex was found, false otherwise. - :rtype: bool - """ - cdef vector[int] csimplex - for i in simplex: - csimplex.push_back(i) - return self.thisptr.find_simplex(csimplex) - - def insert(self, simplex, filtration=0.0): - """This function inserts the given N-simplex and its subfaces with the - given filtration value (default value is '0.0'). If some of those - simplices are already present with a higher filtration value, their - filtration value is lowered. - - :param simplex: The N-simplex to insert, represented by a list of - vertex. - :type simplex: list of int. - :param filtration: The filtration value of the simplex. - :type filtration: float. - :returns: true if the simplex was not yet in the complex, false - otherwise (whatever its original filtration value). - :rtype: bool - """ - cdef vector[int] csimplex - for i in simplex: - csimplex.push_back(i) - return self.thisptr.insert_simplex_and_subfaces(csimplex, - <double>filtration) - - def get_filtration(self): - """This function returns a list of all simplices with their given - filtration values. - - :returns: The simplices sorted by increasing filtration values. - :rtype: list of tuples(simplex, filtration) - """ - cdef vector[pair[vector[int], double]] filtration \ - = self.thisptr.get_filtration() - ct = [] - for filtered_complex in filtration: - v = [] - for vertex in filtered_complex.first: - v.append(vertex) - ct.append((v, filtered_complex.second)) - return ct - - def get_skeleton(self, dimension): - """This function returns the (simplices of the) skeleton of a maximum - given dimension. - - :param dimension: The skeleton dimension value. - :type dimension: int. - :returns: The (simplices of the) skeleton of a maximum dimension. - :rtype: list of tuples(simplex, filtration) - """ - cdef vector[pair[vector[int], double]] skeleton \ - = self.thisptr.get_skeleton(<int>dimension) - ct = [] - for filtered_simplex in skeleton: - v = [] - for vertex in filtered_simplex.first: - v.append(vertex) - ct.append((v, filtered_simplex.second)) - return ct - - def get_star(self, simplex): - """This function returns the star of a given N-simplex. - - :param simplex: The N-simplex, represented by a list of vertex. - :type simplex: list of int. - :returns: The (simplices of the) star of a simplex. - :rtype: list of tuples(simplex, filtration) - """ - cdef vector[int] csimplex - for i in simplex: - csimplex.push_back(i) - cdef vector[pair[vector[int], double]] star \ - = self.thisptr.get_star(csimplex) - ct = [] - for filtered_simplex in star: - v = [] - for vertex in filtered_simplex.first: - v.append(vertex) - ct.append((v, filtered_simplex.second)) - return ct - - def get_cofaces(self, simplex, codimension): - """This function returns the cofaces of a given N-simplex with a - given codimension. - - :param simplex: The N-simplex, represented by a list of vertex. - :type simplex: list of int. - :param codimension: The codimension. If codimension = 0, all cofaces - are returned (equivalent of get_star function) - :type codimension: int. - :returns: The (simplices of the) cofaces of a simplex - :rtype: list of tuples(simplex, filtration) - """ - cdef vector[int] csimplex - for i in simplex: - csimplex.push_back(i) - cdef vector[pair[vector[int], double]] cofaces \ - = self.thisptr.get_cofaces(csimplex, <int>codimension) - ct = [] - for filtered_simplex in cofaces: - v = [] - for vertex in filtered_simplex.first: - v.append(vertex) - ct.append((v, filtered_simplex.second)) - return ct - - def remove_maximal_simplex(self, simplex): - """This function removes a given maximal N-simplex from the simplicial - complex. - - :param simplex: The N-simplex, represented by a list of vertex. - :type simplex: list of int. - - .. note:: - - Be aware that removing is shifting data in a flat_map - (:func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` to be done). - - .. note:: - - The dimension of the simplicial complex may be lower after calling - remove_maximal_simplex than it was before. However, - :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` - method will return the old value, which - remains a valid upper bound. If you care, you can call - :func:`dimension()<gudhi.SimplexTree.dimension>` - to recompute the exact dimension. - """ - self.thisptr.remove_maximal_simplex(simplex) - - def prune_above_filtration(self, filtration): - """Prune above filtration value given as parameter. - - :param filtration: Maximum threshold value. - :type filtration: float. - :returns: The filtration modification information. - :rtype: bint - - - .. note:: - - Some simplex tree functions require the filtration to be valid. - prune_above_filtration function is not launching - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - but returns the filtration modification - information. If the complex has changed , please call - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - to recompute it. - - .. note:: - - Note that the dimension of the simplicial complex may be lower - after calling - :func:`prune_above_filtration()<gudhi.SimplexTree.prune_above_filtration>` - than it was before. However, - :func:`upper_bound_dimension()<gudhi.SimplexTree.upper_bound_dimension>` - will return the old value, which remains a - valid upper bound. If you care, you can call - :func:`dimension()<gudhi.SimplexTree.dimension>` - method to recompute the exact dimension. - """ - return self.thisptr.prune_above_filtration(filtration) - - def expansion(self, max_dim): - """Expands the Simplex_tree containing only its one skeleton - until dimension max_dim. - - The expanded simplicial complex until dimension :math:`d` - attached to a graph :math:`G` is the maximal simplicial complex of - dimension at most :math:`d` admitting the graph :math:`G` as - :math:`1`-skeleton. - The filtration value assigned to a simplex is the maximal filtration - value of one of its edges. - - The Simplex_tree must contain no simplex of dimension bigger than - 1 when calling the method. - - :param max_dim: The maximal dimension. - :type max_dim: int. - """ - self.thisptr.expansion(max_dim) - - def make_filtration_non_decreasing(self): - """This function ensures that each simplex has a higher filtration - value than its faces by increasing the filtration values. - - :returns: The filtration modification information. - :rtype: bint - - - .. note:: - - Some simplex tree functions require the filtration to be valid. - make_filtration_non_decreasing function is not launching - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - but returns the filtration modification - information. If the complex has changed , please call - :func:`initialize_filtration()<gudhi.SimplexTree.initialize_filtration>` - to recompute it. - """ - return self.thisptr.make_filtration_non_decreasing() - - def persistence(self, homology_coeff_field=11, min_persistence=0, persistence_dim_max = False): - """This function returns the persistence of the simplicial complex. - - :param homology_coeff_field: The homology coefficient field. Must be a - prime number. Default value is 11. - :type homology_coeff_field: int. - :param min_persistence: The minimum persistence value to take into - account (strictly greater than min_persistence). Default value is - 0.0. - Sets min_persistence to -1.0 to see all values. - :type min_persistence: float. - :returns: The persistence of the simplicial complex. - :rtype: list of pairs(dimension, pair(birth, death)) - """ - if self.pcohptr != NULL: - del self.pcohptr - self.pcohptr = new Simplex_tree_persistence_interface(self.thisptr, persistence_dim_max) - cdef vector[pair[int, pair[double, double]]] persistence_result - if self.pcohptr != NULL: - persistence_result = self.pcohptr.get_persistence(homology_coeff_field, min_persistence) - return persistence_result - - def betti_numbers(self): - """This function returns the Betti numbers of the simplicial complex. - - :returns: The Betti numbers ([B0, B1, ..., Bn]). - :rtype: list of int - - :note: betti_numbers function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` - function to be launched first. - """ - cdef vector[int] bn_result - if self.pcohptr != NULL: - bn_result = self.pcohptr.betti_numbers() - else: - print("betti_numbers function requires persistence function" - " to be launched first.") - return bn_result - - def persistent_betti_numbers(self, from_value, to_value): - """This function returns the persistent Betti numbers of the - simplicial complex. - - :param from_value: The persistence birth limit to be added in the - numbers (persistent birth <= from_value). - :type from_value: float. - :param to_value: The persistence death limit to be added in the - numbers (persistent death > to_value). - :type to_value: float. - - :returns: The persistent Betti numbers ([B0, B1, ..., Bn]). - :rtype: list of int - - :note: persistent_betti_numbers function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` - function to be launched first. - """ - cdef vector[int] pbn_result - if self.pcohptr != NULL: - pbn_result = self.pcohptr.persistent_betti_numbers(<double>from_value, <double>to_value) - else: - print("persistent_betti_numbers function requires persistence function" - " to be launched first.") - return pbn_result - - def persistence_intervals_in_dimension(self, dimension): - """This function returns the persistence intervals of the simplicial - complex in a specific dimension. - - :param dimension: The specific dimension. - :type dimension: int. - :returns: The persistence intervals. - :rtype: list of pair of float - - :note: intervals_in_dim function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` - function to be launched first. - """ - cdef vector[pair[double,double]] intervals_result - if self.pcohptr != NULL: - intervals_result = self.pcohptr.intervals_in_dimension(dimension) - else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") - return intervals_result - - def persistence_pairs(self): - """This function returns the persistence pairs of the simplicial - complex. - - :returns: The persistence intervals. - :rtype: list of pair of list of int - - :note: intervals_in_dim function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` - function to be launched first. - """ - cdef vector[pair[vector[int],vector[int]]] persistence_pairs_result - if self.pcohptr != NULL: - persistence_pairs_result = self.pcohptr.persistence_pairs() - else: - print("persistence_pairs function requires persistence function" - " to be launched first.") - return persistence_pairs_result - - def write_persistence_diagram(self, persistence_file=''): - """This function writes the persistence intervals of the simplicial - complex in a user given file name. - - :param persistence_file: The specific dimension. - :type persistence_file: string. - - :note: intervals_in_dim function requires - :func:`persistence()<gudhi.SimplexTree.persistence>` - function to be launched first. - """ - if self.pcohptr != NULL: - if persistence_file != '': - self.pcohptr.write_output_diagram(str.encode(persistence_file)) - else: - print("persistence_file must be specified") - else: - print("intervals_in_dim function requires persistence function" - " to be launched first.") diff --git a/cython/cython/strong_witness_complex.pyx b/cython/cython/strong_witness_complex.pyx deleted file mode 100644 index 74c5cb05..00000000 --- a/cython/cython/strong_witness_complex.pyx +++ /dev/null @@ -1,81 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Strong_witness_complex_interface.h" namespace "Gudhi": - cdef cppclass Strong_witness_complex_interface "Gudhi::witness_complex::Strong_witness_complex_interface": - Strong_witness_complex_interface(vector[vector[pair[size_t, double]]] nearest_landmark_table) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) - -# StrongWitnessComplex python interface -cdef class StrongWitnessComplex: - """Constructs (strong) witness complex for a given table of nearest - landmarks with respect to witnesses. - """ - - cdef Strong_witness_complex_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, nearest_landmark_table=None): - """StrongWitnessComplex constructor. - - :param nearest_landmark_table: A list of nearest landmark. - :type nearest_landmark_table: list of list of pair of unsigned and double - """ - - # The real cython constructor - def __cinit__(self, nearest_landmark_table=None): - if nearest_landmark_table is not None: - self.thisptr = new Strong_witness_complex_interface(nearest_landmark_table) - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if StrongWitnessComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def create_simplex_tree(self, max_alpha_square, limit_dimension = -1): - """ - :param max_alpha_square: The maximum alpha square threshold the - simplices shall not exceed. Default is set to infinity. - :type max_alpha_square: float - :returns: A simplex tree created from the Delaunay Triangulation. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - if limit_dimension is not -1: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square, limit_dimension) - else: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square) - return simplex_tree diff --git a/cython/cython/subsampling.pyx b/cython/cython/subsampling.pyx deleted file mode 100644 index e9d61a37..00000000 --- a/cython/cython/subsampling.pyx +++ /dev/null @@ -1,142 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Subsampling_interface.h" namespace "Gudhi::subsampling": - vector[vector[double]] subsampling_n_farthest_points(vector[vector[double]] points, unsigned nb_points) - vector[vector[double]] subsampling_n_farthest_points(vector[vector[double]] points, unsigned nb_points, unsigned starting_point) - vector[vector[double]] subsampling_n_farthest_points_from_file(string off_file, unsigned nb_points) - vector[vector[double]] subsampling_n_farthest_points_from_file(string off_file, unsigned nb_points, unsigned starting_point) - vector[vector[double]] subsampling_n_random_points(vector[vector[double]] points, unsigned nb_points) - vector[vector[double]] subsampling_n_random_points_from_file(string off_file, unsigned nb_points) - vector[vector[double]] subsampling_sparsify_points(vector[vector[double]] points, double min_squared_dist) - vector[vector[double]] subsampling_sparsify_points_from_file(string off_file, double min_squared_dist) - -def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_point = ''): - """Subsample by a greedy strategy of iteratively adding the farthest point - from the current chosen point set to the subsampling. - The iteration starts with the landmark `starting point`. - - :param points: The input point set. - :type points: vector[vector[double]]. - - Or - - :param off_file: An OFF file style name. - :type off_file: string - - :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. - :param starting_point: The iteration starts with the landmark `starting \ - point`,which is the index of the poit to start with. If not set, this \ - index is choosen randomly. - :type starting_point: unsigned. - :returns: The subsample point set. - :rtype: vector[vector[double]] - """ - if off_file is not '': - if os.path.isfile(off_file): - if starting_point is '': - return subsampling_n_farthest_points_from_file(str.encode(off_file), - nb_points) - else: - return subsampling_n_farthest_points_from_file(str.encode(off_file), - nb_points, - starting_point) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty points - points=[] - if starting_point is '': - return subsampling_n_farthest_points(points, nb_points) - else: - return subsampling_n_farthest_points(points, nb_points, - starting_point) - -def pick_n_random_points(points=None, off_file='', nb_points=0): - """Subsample a point set by picking random vertices. - - :param points: The input point set. - :type points: vector[vector[double]]. - - Or - - :param off_file: An OFF file style name. - :type off_file: string - - :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. - :returns: The subsample point set. - :rtype: vector[vector[double]] - """ - if off_file is not '': - if os.path.isfile(off_file): - return subsampling_n_random_points_from_file(str.encode(off_file), - nb_points) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty points - points=[] - return subsampling_n_random_points(points, nb_points) - -def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): - """Outputs a subset of the input points so that the squared distance - between any two points is greater than or equal to min_squared_dist. - - :param points: The input point set. - :type points: vector[vector[double]]. - - Or - - :param off_file: An OFF file style name. - :type off_file: string - - :param min_squared_dist: Minimum squared distance separating the output \ - points. - :type min_squared_dist: float. - :returns: The subsample point set. - :rtype: vector[vector[double]] - """ - if off_file is not '': - if os.path.isfile(off_file): - return subsampling_sparsify_points_from_file(str.encode(off_file), - min_squared_dist) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty points - points=[] - return subsampling_sparsify_points(points, min_squared_dist) diff --git a/cython/cython/tangential_complex.pyx b/cython/cython/tangential_complex.pyx deleted file mode 100644 index 4bb07076..00000000 --- a/cython/cython/tangential_complex.pyx +++ /dev/null @@ -1,154 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Tangential_complex_interface.h" namespace "Gudhi": - cdef cppclass Tangential_complex_interface "Gudhi::tangential_complex::Tangential_complex_interface": - Tangential_complex_interface(int intrisic_dim, vector[vector[double]] points) - # bool from_file is a workaround for cython to find the correct signature - Tangential_complex_interface(int intrisic_dim, string off_file, bool from_file) - vector[double] get_point(unsigned vertex) - unsigned number_of_vertices() - unsigned number_of_simplices() - unsigned number_of_inconsistent_simplices() - unsigned number_of_inconsistent_stars() - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree) - void fix_inconsistencies_using_perturbation(double max_perturb, double time_limit) - -# TangentialComplex python interface -cdef class TangentialComplex: - """The class Tangential_complex represents a tangential complex. After the - computation of the complex, an optional post-processing called perturbation - can be run to attempt to remove inconsistencies. - """ - - cdef Tangential_complex_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, intrisic_dim, points=None, off_file=''): - """TangentialComplex constructor. - - :param intrisic_dim: Intrinsic dimension of the manifold. - :type intrisic_dim: integer - - :param points: A list of points in d-Dimension. - :type points: list of list of double - - Or - - :param off_file: An OFF file style name. - :type off_file: string - """ - - # The real cython constructor - def __cinit__(self, intrisic_dim, points=None, off_file=''): - if off_file is not '': - if os.path.isfile(off_file): - self.thisptr = new Tangential_complex_interface(intrisic_dim, str.encode(off_file), True) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty tangential construction - points=[] - self.thisptr = new Tangential_complex_interface(intrisic_dim, points) - - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if TangentialComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def get_point(self, vertex): - """This function returns the point corresponding to a given vertex. - - :param vertex: The vertex. - :type vertex: int. - :returns: The point. - :rtype: list of float - """ - cdef vector[double] point = self.thisptr.get_point(vertex) - return point - - def num_vertices(self): - """ - :returns: The number of vertices. - :rtype: unsigned - """ - return self.thisptr.number_of_vertices() - - def num_simplices(self): - """ - :returns: Total number of simplices in stars (including duplicates that appear in several stars). - :rtype: unsigned - """ - return self.thisptr.number_of_simplices() - - def num_inconsistent_simplices(self): - """ - :returns: The number of inconsistent simplices. - :rtype: unsigned - """ - return self.thisptr.number_of_inconsistent_simplices() - - def num_inconsistent_stars(self): - """ - :returns: The number of stars containing at least one inconsistent simplex. - :rtype: unsigned - """ - return self.thisptr.number_of_inconsistent_stars() - - def create_simplex_tree(self): - """Exports the complex into a simplex tree. - - :returns: A simplex tree created from the complex. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - self.thisptr.create_simplex_tree(simplex_tree.thisptr) - return simplex_tree - - def fix_inconsistencies_using_perturbation(self, max_perturb, time_limit=-1.0): - """Attempts to fix inconsistencies by perturbing the point positions. - - :param max_perturb: Maximum length of the translations used by the - perturbation. - :type max_perturb: double - :param time_limit: Time limit in seconds. If -1, no time limit is set. - :type time_limit: double - """ - self.thisptr.fix_inconsistencies_using_perturbation(max_perturb, - time_limit) diff --git a/cython/cython/witness_complex.pyx b/cython/cython/witness_complex.pyx deleted file mode 100644 index 8591465a..00000000 --- a/cython/cython/witness_complex.pyx +++ /dev/null @@ -1,81 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.utility cimport pair - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Witness_complex_interface.h" namespace "Gudhi": - cdef cppclass Witness_complex_interface "Gudhi::witness_complex::Witness_complex_interface": - Witness_complex_interface(vector[vector[pair[size_t, double]]] nearest_landmark_table) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square) - void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, double max_alpha_square, - unsigned limit_dimension) - -# WitnessComplex python interface -cdef class WitnessComplex: - """Constructs (weak) witness complex for a given table of nearest landmarks - with respect to witnesses. - """ - - cdef Witness_complex_interface * thisptr - - # Fake constructor that does nothing but documenting the constructor - def __init__(self, nearest_landmark_table=None): - """WitnessComplex constructor. - - :param nearest_landmark_table: A list of nearest landmark. - :type nearest_landmark_table: list of list of pair of unsigned and double - """ - - # The real cython constructor - def __cinit__(self, nearest_landmark_table=None): - if nearest_landmark_table is not None: - self.thisptr = new Witness_complex_interface(nearest_landmark_table) - - def __dealloc__(self): - if self.thisptr != NULL: - del self.thisptr - - def __is_defined(self): - """Returns true if WitnessComplex pointer is not NULL. - """ - return self.thisptr != NULL - - def create_simplex_tree(self, max_alpha_square, limit_dimension = -1): - """ - :param max_alpha_square: The maximum alpha square threshold the - simplices shall not exceed. Default is set to infinity. - :type max_alpha_square: float - :returns: A simplex tree created from the Delaunay Triangulation. - :rtype: SimplexTree - """ - simplex_tree = SimplexTree() - if limit_dimension is not -1: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square, limit_dimension) - else: - self.thisptr.create_simplex_tree(simplex_tree.thisptr, max_alpha_square) - return simplex_tree diff --git a/cython/doc/_templates/layout.html b/cython/doc/_templates/layout.html deleted file mode 100644 index bc0e9658..00000000 --- a/cython/doc/_templates/layout.html +++ /dev/null @@ -1,275 +0,0 @@ -{# - basic/layout.html - ~~~~~~~~~~~~~~~~~ - - Master layout template for Sphinx themes. - - :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS. - :license: BSD, see LICENSE for details. -#} -{%- block doctype -%} -<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" - "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> -{%- endblock %} -{%- set reldelim1 = reldelim1 is not defined and ' »' or reldelim1 %} -{%- set reldelim2 = reldelim2 is not defined and ' |' or reldelim2 %} -{%- set render_sidebar = (not embedded) and (not theme_nosidebar|tobool) and - (sidebars != []) %} -{%- set url_root = pathto('', 1) %} -{# XXX necessary? #} -{%- if url_root == '#' %}{% set url_root = '' %}{% endif %} -{%- if not embedded and docstitle %} - {%- set titlesuffix = " — "|safe + docstitle|e %} -{%- else %} - {%- set titlesuffix = "" %} -{%- endif %} - -{%- macro relbar() %} - <div class="related" role="navigation" aria-label="related navigation"> - <h3>{{ _('Navigation') }}</h3> - <ul> - {%- for rellink in rellinks %} - <li class="right" {% if loop.first %}style="margin-right: 10px"{% endif %}> - <a href="{{ pathto(rellink[0]) }}" title="{{ rellink[1]|striptags|e }}" - {{ accesskey(rellink[2]) }}>{{ rellink[3] }}</a> - {%- if not loop.first %}{{ reldelim2 }}{% endif %}</li> - {%- endfor %} - {%- block rootrellink %} - <li class="nav-item nav-item-0"><a href="{{ pathto(master_doc) }}">{{ shorttitle|e }}</a>{{ reldelim1 }}</li> - {%- endblock %} - {%- for parent in parents %} - <li class="nav-item nav-item-{{ loop.index }}"><a href="{{ parent.link|e }}" {% if loop.last %}{{ accesskey("U") }}{% endif %}>{{ parent.title }}</a>{{ reldelim1 }}</li> - {%- endfor %} - {%- block relbaritems %} {% endblock %} - </ul> - </div> -{%- endmacro %} - -{%- macro sidebar() %} - {%- if render_sidebar %} - <div class="sphinxsidebar" role="navigation" aria-label="main navigation"> - <div class="sphinxsidebarwrapper"> - {%- block sidebarlogo %} - {%- if logo %} - <p class="logo"><a href="{{ pathto(master_doc) }}"> - <img class="logo" src="{{ pathto('_static/' + logo, 1) }}" alt="Logo"/> - </a></p> - {%- endif %} - {%- endblock %} - <h2><a href="index.html">GUDHI</a></h2> - <h2><a href="fileformats.html">File formats</a></h2> - <h2><a href="installation.html">GUDHI installation</a></h2> - <h2><a href="citation.html">Acknowledging the GUDHI library</a></h2> - <h2><a href="genindex.html">Index</a></h2> - <h2><a href="examples.html">Examples</a></h2> - {%- if sidebars != None %} - {#- new style sidebar: explicitly include/exclude templates #} - {%- for sidebartemplate in sidebars %} - {%- include sidebartemplate %} - {%- endfor %} - {%- else %} - {#- old style sidebars: using blocks -- should be deprecated #} - {%- block sidebartoc %} - {%- include "localtoc.html" %} - {%- endblock %} - {%- block sidebarrel %} - {%- include "relations.html" %} - {%- endblock %} - {%- block sidebarsourcelink %} - {%- include "sourcelink.html" %} - {%- endblock %} - {%- if customsidebar %} - {%- include customsidebar %} - {%- endif %} - {%- block sidebarsearch %} - {%- include "searchbox.html" %} - {%- endblock %} - {%- endif %} - </div> - </div> - {%- endif %} -{%- endmacro %} - -{%- macro script() %} - <script type="text/javascript"> - var DOCUMENTATION_OPTIONS = { - URL_ROOT: '{{ url_root }}', - VERSION: '{{ release|e }}', - COLLAPSE_INDEX: false, - FILE_SUFFIX: '{{ '' if no_search_suffix else file_suffix }}', - HAS_SOURCE: {{ has_source|lower }} - }; 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To get rid of WARNING: document isn't included in any toctree - -============================== -Alpha complex reference manual -============================== - -.. autoclass:: gudhi.AlphaComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.AlphaComplex.__init__ diff --git a/cython/doc/alpha_complex_sum.inc b/cython/doc/alpha_complex_sum.inc deleted file mode 100644 index 1680a712..00000000 --- a/cython/doc/alpha_complex_sum.inc +++ /dev/null @@ -1,22 +0,0 @@ -================================================================= =================================== =================================== -:Author: Vincent Rouvreau :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -:Requires: CGAL :math:`\geq` 4.7.0 Eigen3 -================================================================= =================================== =================================== - -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| .. figure:: | Alpha_complex is a simplicial complex constructed from the finite | -| ../../doc/Alpha_complex/alpha_complex_representation.png | cells of a Delaunay Triangulation. | -| :alt: Alpha complex representation | | -| :figclass: align-center | The filtration value of each simplex is computed as the square of the | -| | circumradius of the simplex if the circumsphere is empty (the simplex | -| Alpha complex representation | is then said to be Gabriel), and as the minimum of the filtration | -| | values of the codimension 1 cofaces that make it not Gabriel | -| | otherwise. All simplices that have a filtration value strictly | -| | greater than a given alpha squared value are not inserted into the | -| | complex. | -| | | -| | This package requires having CGAL version 4.7 or higher (4.8.1 is | -| | advised for better performance). | -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| :doc:`alpha_complex_user` | :doc:`alpha_complex_ref` | -+----------------------------------------------------------------+------------------------------------------------------------------------+ diff --git a/cython/doc/alpha_complex_user.rst b/cython/doc/alpha_complex_user.rst deleted file mode 100644 index d1e9c7cd..00000000 --- a/cython/doc/alpha_complex_user.rst +++ /dev/null @@ -1,210 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Alpha complex user manual -========================= -Definition ----------- - -.. include:: alpha_complex_sum.inc - -Alpha_complex is constructing a :doc:`Simplex_tree <simplex_tree_ref>` using -`Delaunay Triangulation <http://doc.cgal.org/latest/Triangulation/index.html#Chapter_Triangulations>`_ -:cite:`cgal:hdj-t-15b` from `CGAL <http://www.cgal.org/>`_ (the Computational Geometry Algorithms Library -:cite:`cgal:eb-15b`). - -Remarks -^^^^^^^ -When Alpha_complex is constructed with an infinite value of :math:`\alpha`, the complex is a Delaunay complex. - -Example from points -------------------- - -This example builds the Delaunay triangulation from the given points, and initializes the alpha complex with it: - -.. testcode:: - - import gudhi - alpha_complex = gudhi.AlphaComplex(points=[[1, 1], [7, 0], [4, 6], [9, 6], [0, 14], [2, 19], [9, 17]]) - - simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=60.0) - result_str = 'Alpha complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - fmt = '%s -> %.2f' - for filtered_value in simplex_tree.get_filtration(): - print(fmt % tuple(filtered_value)) - -The output is: - -.. testoutput:: - - Alpha complex is of dimension 2 - 25 simplices - 7 vertices. - [0] -> 0.00 - [1] -> 0.00 - [2] -> 0.00 - [3] -> 0.00 - [4] -> 0.00 - [5] -> 0.00 - [6] -> 0.00 - [2, 3] -> 6.25 - [4, 5] -> 7.25 - [0, 2] -> 8.50 - [0, 1] -> 9.25 - [1, 3] -> 10.00 - [1, 2] -> 11.25 - [1, 2, 3] -> 12.50 - [0, 1, 2] -> 13.00 - [5, 6] -> 13.25 - [2, 4] -> 20.00 - [4, 6] -> 22.74 - [4, 5, 6] -> 22.74 - [3, 6] -> 30.25 - [2, 6] -> 36.50 - [2, 3, 6] -> 36.50 - [2, 4, 6] -> 37.24 - [0, 4] -> 59.71 - [0, 2, 4] -> 59.71 - - -Algorithm ---------- - -Data structure -^^^^^^^^^^^^^^ - -In order to build the alpha complex, first, a Simplex tree is built from the cells of a Delaunay Triangulation. -(The filtration value is set to NaN, which stands for unknown value): - -.. figure:: - ../../doc/Alpha_complex/alpha_complex_doc.png - :figclass: align-center - :alt: Simplex tree structure construction example - - Simplex tree structure construction example - -Filtration value computation algorithm -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - **for** i : dimension :math:`\rightarrow` 0 **do** - **for all** :math:`\sigma` of dimension i - **if** filtration(:math:`\sigma`) is NaN **then** - filtration(:math:`\sigma`) = :math:`\alpha^2(\sigma)` - **end if** - - *//propagate alpha filtration value* - - **for all** :math:`\tau` face of :math:`\sigma` - **if** filtration(:math:`\tau`) is not NaN **then** - filtration(:math:`\tau`) = filtration(:math:`\sigma`) - **end if** - **end for** - **end for** - **end for** - - make_filtration_non_decreasing() - - prune_above_filtration() - -Dimension 2 -^^^^^^^^^^^ - -From the example above, it means the algorithm looks into each triangle ([0,1,2], [0,2,4], [1,2,3], ...), -computes the filtration value of the triangle, and then propagates the filtration value as described -here: - -.. figure:: - ../../doc/Alpha_complex/alpha_complex_doc_420.png - :figclass: align-center - :alt: Filtration value propagation example - - Filtration value propagation example - -Dimension 1 -^^^^^^^^^^^ - -Then, the algorithm looks into each edge ([0,1], [0,2], [1,2], ...), -computes the filtration value of the edge (in this case, propagation will have no effect). - -Dimension 0 -^^^^^^^^^^^ - -Finally, the algorithm looks into each vertex ([0], [1], [2], [3], [4], [5] and [6]) and -sets the filtration value (0 in case of a vertex - propagation will have no effect). - -Non decreasing filtration values -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -As the squared radii computed by CGAL are an approximation, it might happen that these alpha squared values do not -quite define a proper filtration (i.e. non-decreasing with respect to inclusion). -We fix that up by calling `Simplex_tree::make_filtration_non_decreasing()` (cf. -`C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). - -Prune above given filtration value -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The simplex tree is pruned from the given maximum alpha squared value (cf. `Simplex_tree::prune_above_filtration()` -in the `C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). -In the following example, the value is given by the user as argument of the program. - - -Example from OFF file -^^^^^^^^^^^^^^^^^^^^^ - -This example builds the Delaunay triangulation from the points given by an OFF file, and initializes the alpha complex -with it. - - -Then, it is asked to display information about the alpha complex: - -.. testcode:: - - import gudhi - alpha_complex = gudhi.AlphaComplex(off_file=gudhi.__root_source_dir__ + \ - '/data/points/alphacomplexdoc.off') - simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=59.0) - result_str = 'Alpha complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - fmt = '%s -> %.2f' - for filtered_value in simplex_tree.get_filtration(): - print(fmt % tuple(filtered_value)) - -the program output is: - -.. testoutput:: - - Alpha complex is of dimension 2 - 23 simplices - 7 vertices. - [0] -> 0.00 - [1] -> 0.00 - [2] -> 0.00 - [3] -> 0.00 - [4] -> 0.00 - [5] -> 0.00 - [6] -> 0.00 - [2, 3] -> 6.25 - [4, 5] -> 7.25 - [0, 2] -> 8.50 - [0, 1] -> 9.25 - [1, 3] -> 10.00 - [1, 2] -> 11.25 - [1, 2, 3] -> 12.50 - [0, 1, 2] -> 13.00 - [5, 6] -> 13.25 - [2, 4] -> 20.00 - [4, 6] -> 22.74 - [4, 5, 6] -> 22.74 - [3, 6] -> 30.25 - [2, 6] -> 36.50 - [2, 3, 6] -> 36.50 - [2, 4, 6] -> 37.24 - -CGAL citations -============== - -.. bibliography:: ../../biblio/how_to_cite_cgal.bib - :filter: docnames - :style: unsrt diff --git a/cython/doc/bottleneck_distance_sum.inc b/cython/doc/bottleneck_distance_sum.inc deleted file mode 100644 index 030fad9e..00000000 --- a/cython/doc/bottleneck_distance_sum.inc +++ /dev/null @@ -1,15 +0,0 @@ -================================================================= =================================== =================================== -:Author: François Godi :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -:Requires: CGAL :math:`\geq` 4.8.0 -================================================================= =================================== =================================== - -+-----------------------------------------------------------------+----------------------------------------------------------------------+ -| .. figure:: | Bottleneck distance measures the similarity between two persistence | -| ../../doc/Bottleneck_distance/perturb_pd.png | diagrams. It's the shortest distance b for which there exists a | -| :figclass: align-center | perfect matching between the points of the two diagrams (+ all the | -| | diagonal points) such that any couple of matched points are at | -| Bottleneck distance is the length of | distance at most b. | -| the longest edge | | -+-----------------------------------------------------------------+----------------------------------------------------------------------+ -| :doc:`bottleneck_distance_user` | | -+-----------------------------------------------------------------+----------------------------------------------------------------------+ diff --git a/cython/doc/bottleneck_distance_user.rst b/cython/doc/bottleneck_distance_user.rst deleted file mode 100644 index 605db022..00000000 --- a/cython/doc/bottleneck_distance_user.rst +++ /dev/null @@ -1,40 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Bottleneck distance user manual -=============================== -Definition ----------- - -.. include:: bottleneck_distance_sum.inc - -Function --------- -.. autofunction:: gudhi.bottleneck_distance - - -Basic example -------------- - -This example computes the bottleneck distance from 2 persistence diagrams: - -.. testcode:: - - import gudhi - - diag1 = [[2.7, 3.7],[9.6, 14.],[34.2, 34.974], [3.,float('Inf')]] - diag2 = [[2.8, 4.45],[9.5, 14.1],[3.2,float('Inf')]] - - message = "Bottleneck distance approximation=" + '%.2f' % gudhi.bottleneck_distance(diag1, diag2, 0.1) - print(message) - - message = "Bottleneck distance value=" + '%.2f' % gudhi.bottleneck_distance(diag1, diag2) - print(message) - -The output is: - -.. testoutput:: - - Bottleneck distance approximation=0.81 - Bottleneck distance value=0.75 diff --git a/cython/doc/citation.rst b/cython/doc/citation.rst deleted file mode 100644 index 117eb9dd..00000000 --- a/cython/doc/citation.rst +++ /dev/null @@ -1,19 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Acknowledging the GUDHI library -############################### - -We kindly ask users to cite the GUDHI library as appropriately as possible in -their papers, and to mention the use of the GUDHI library on the web pages of -their projects using GUDHI and provide us with links to these web pages. Feel -free to contact us in case you have any question or remark on this topic. - -We provide GUDHI bibtex entries for the modules of the User and Reference -Manual, as well as for publications directly related to the GUDHI library. - -GUDHI bibtex -************ - -.. literalinclude:: ../../biblio/how_to_cite_gudhi.bib diff --git a/cython/doc/conf.py b/cython/doc/conf.py deleted file mode 100755 index 4a54d4fd..00000000 --- a/cython/doc/conf.py +++ /dev/null @@ -1,203 +0,0 @@ -# -*- coding: utf-8 -*- -# -# GUDHI documentation build configuration file, created by -# sphinx-quickstart on Thu Jun 30 09:55:51 2016. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import sys -import os - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -#sys.path.insert(0, os.path.abspath('.')) - -# Path to Gudhi.so from source path -sys.path.insert(0, os.path.abspath('.')) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'matplotlib.sphinxext.plot_directive', - 'sphinx.ext.autodoc', - 'sphinx.ext.doctest', - 'sphinx.ext.todo', - 'sphinx.ext.mathjax', - 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode', - 'sphinxcontrib.bibtex', -] - -todo_include_todos = True -# plot option : do not show hyperlinks (Source code, png, hires.png, pdf) -plot_html_show_source_link = False -plot_html_show_formats = False -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix of source filenames. -source_suffix = '.rst' - -# The encoding of source files. -#source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -import gudhi - -# General information about the project. -project = gudhi.__name__ -copyright = gudhi.__copyright__ + ' - ' + gudhi.__license__ - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = gudhi.__version__ -# The full version, including alpha/beta/rc tags. -#release = '2.0.1-rc1' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -#language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -#today = '' -# Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ['_build', '*.inc'] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -#default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -#add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -#show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -#keep_warnings = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = 'classic' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -html_theme_options = { - "sidebarbgcolor": "#A1ADCD", - "sidebartextcolor": "black", - "sidebarlinkcolor": "#334D5C", - "body_max_width": "1200px", -} - -# Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# "<project> v<release> documentation". -#html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -#html_logo = - -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -#html_favicon = - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -#html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -#html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -#html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -#html_additional_pages = {'installation': 'installation.html'} - -# If false, no module index is generated. -#html_domain_indices = True - -# If false, no index is generated. -#html_use_index = True - -# If true, the index is split into individual pages for each letter. -#html_split_index = False - -# If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a <link> tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -#html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None - -# Output file base name for HTML help builder. -htmlhelp_basename = 'GUDHIdoc' - diff --git a/cython/doc/cubical_complex_ref.rst b/cython/doc/cubical_complex_ref.rst deleted file mode 100644 index 1fe9d5fb..00000000 --- a/cython/doc/cubical_complex_ref.rst +++ /dev/null @@ -1,13 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Cubical complex reference manual -################################ - -.. autoclass:: gudhi.CubicalComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.CubicalComplex.__init__ diff --git a/cython/doc/cubical_complex_sum.inc b/cython/doc/cubical_complex_sum.inc deleted file mode 100644 index 280ad0e0..00000000 --- a/cython/doc/cubical_complex_sum.inc +++ /dev/null @@ -1,15 +0,0 @@ -================================================================= =================================== =================================== -:Author: Pawel Dlotko :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -================================================================= =================================== =================================== - -+--------------------------------------------------------------------------+----------------------------------------------------------------------+ -| .. figure:: | The cubical complex is an example of a structured complex useful in | -| ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png | computational mathematics (specially rigorous numerics) and image | -| :alt: Cubical complex representation | analysis. | -| :figclass: align-center | | -| | | -| Cubical complex representation | | -+--------------------------------------------------------------------------+----------------------------------------------------------------------+ -| :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | -| | * :doc:`periodic_cubical_complex_ref` | -+--------------------------------------------------------------------------+----------------------------------------------------------------------+ diff --git a/cython/doc/cubical_complex_user.rst b/cython/doc/cubical_complex_user.rst deleted file mode 100644 index 320bd79b..00000000 --- a/cython/doc/cubical_complex_user.rst +++ /dev/null @@ -1,162 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Cubical complex user manual -=========================== -Definition ----------- - -===================================== ===================================== ===================================== -:Author: Pawel Dlotko :Introduced in: GUDHI PYTHON 2.0.0 :Copyright: GPL v3 -===================================== ===================================== ===================================== - -+---------------------------------------------+----------------------------------------------------------------------+ -| :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | -| | * :doc:`periodic_cubical_complex_ref` | -+---------------------------------------------+----------------------------------------------------------------------+ - -The cubical complex is an example of a structured complex useful in computational mathematics (specially rigorous -numerics) and image analysis. - -An *elementary interval* is an interval of a form :math:`[n,n+1]`, or :math:`[n,n]`, for :math:`n \in \mathcal{Z}`. -The first one is called *non-degenerate*, while the second one is a *degenerate* interval. A -*boundary of a elementary interval* is a chain :math:`\partial [n,n+1] = [n+1,n+1]-[n,n]` in case of -non-degenerated elementary interval and :math:`\partial [n,n] = 0` in case of degenerate elementary interval. An -*elementary cube* :math:`C` is a product of elementary intervals, :math:`C=I_1 \times \ldots \times I_n`. -*Embedding dimension* of a cube is n, the number of elementary intervals (degenerate or not) in the product. -A *dimension of a cube* :math:`C=I_1 \times ... \times I_n` is the number of non degenerate elementary -intervals in the product. A *boundary of a cube* :math:`C=I_1 \times \ldots \times I_n` is a chain obtained -in the following way: - -.. math:: - - \partial C = (\partial I_1 \times \ldots \times I_n) + (I_1 \times \partial I_2 \times \ldots \times I_n) + - \ldots + (I_1 \times I_2 \times \ldots \times \partial I_n). - -A *cubical complex* :math:`\mathcal{K}` is a collection of cubes closed under operation of taking boundary -(i.e. boundary of every cube from the collection is in the collection). A cube :math:`C` in cubical complex -:math:`\mathcal{K}` is *maximal* if it is not in a boundary of any other cube in :math:`\mathcal{K}`. A -*support* of a cube :math:`C` is the set in :math:`\mathbb{R}^n` occupied by :math:`C` (:math:`n` is the embedding -dimension of :math:`C`). - -Cubes may be equipped with a filtration values in which case we have filtered cubical complex. All the cubical -complexes considered in this implementation are filtered cubical complexes (although, the range of a filtration may -be a set of two elements). - -For further details and theory of cubical complexes, please consult :cite:`kaczynski2004computational` as well as the -following paper :cite:`peikert2012topological`. - -Data structure. ---------------- - -The implementation of Cubical complex provides a representation of complexes that occupy a rectangular region in -:math:`\mathbb{R}^n`. This extra assumption allows for a memory efficient way of storing cubical complexes in a form -of so called bitmaps. Let -:math:`R = [b_1,e_1] \times \ldots \times [b_n,e_n]`, for :math:`b_1,...b_n,e_1,...,e_n \in \mathbb{Z}`, -:math:`b_i \leq d_i` be the considered rectangular region and let :math:`\mathcal{K}` be a filtered -cubical complex having the rectangle :math:`R` as its support. Note that the structure of the coordinate system gives -a way a lexicographical ordering of cells of :math:`\mathcal{K}`. This ordering is a base of the presented -bitmap-based implementation. In this implementation, the whole cubical complex is stored as a vector of the values -of filtration. This, together with dimension of :math:`\mathcal{K}` and the sizes of :math:`\mathcal{K}` in all -directions, allows to determine, dimension, neighborhood, boundary and coboundary of every cube -:math:`C \in \mathcal{K}`. - -.. figure:: - ../../doc/Bitmap_cubical_complex/Cubical_complex_representation.png - :alt: Cubical complex. - :figclass: align-center - - Cubical complex. - -Note that the cubical complex in the figure above is, in a natural way, a product of one dimensional cubical -complexes in :math:`\mathbb{R}`. The number of all cubes in each direction is equal :math:`2n+1`, where :math:`n` is -the number of maximal cubes in the considered direction. Let us consider a cube at the position :math:`k` in the -bitmap. -Knowing the sizes of the bitmap, by a series of modulo operation, we can determine which elementary intervals are -present in the product that gives the cube :math:`C`. In a similar way, we can compute boundary and the coboundary of -each cube. Further details can be found in the literature. - -Input Format. -------------- - -In the current implantation, filtration is given at the maximal cubes, and it is then extended by the lower star -filtration to all cubes. There are a number of constructors that can be used to construct cubical complex by users -who want to use the code directly. They can be found in the :doc:`cubical_complex_ref`. -Currently one input from a text file is used. It uses a format used already in -`Perseus software <http://www.sas.upenn.edu/~vnanda/perseus/>`_ by Vidit Nanda. -The file format is described here: :doc:`Perseus <fileformats>`. - -.. testcode:: - - import gudhi - cubical_complex = gudhi.CubicalComplex(perseus_file=gudhi.__root_source_dir__ + \ - '/data/bitmap/cubicalcomplexdoc.txt') - result_str = 'Cubical complex is of dimension ' + repr(cubical_complex.dimension()) + ' - ' + \ - repr(cubical_complex.num_simplices()) + ' simplices.' - print(result_str) - -the program output is: - -.. testoutput:: - - Cubical complex is of dimension 2 - 49 simplices. - -Periodic boundary conditions. ------------------------------ - -Often one would like to impose periodic boundary conditions to the cubical complex (cf. -:doc:`periodic_cubical_complex_ref`). -Let :math:`I_1\times ... \times I_n` be a box that is decomposed with a cubical complex :math:`\mathcal{K}`. -Imposing periodic boundary conditions in the direction i, means that the left and the right side of a complex -:math:`\mathcal{K}` are considered the same. In particular, if for a bitmap :math:`\mathcal{K}` periodic boundary -conditions are imposed in all directions, then complex :math:`\mathcal{K}` became n-dimensional torus. One can use -various constructors from the file Bitmap_cubical_complex_periodic_boundary_conditions_base.h to construct cubical -complex with periodic boundary conditions. - -One can also use Perseus style input files (see :doc:`Perseus <fileformats>`) for the specific periodic case: - -.. testcode:: - - import gudhi - periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file=gudhi.__root_source_dir__ + \ - '/data/bitmap/periodiccubicalcomplexdoc.txt') - result_str = 'Periodic cubical complex is of dimension ' + repr(periodic_cc.dimension()) + ' - ' + \ - repr(periodic_cc.num_simplices()) + ' simplices.' - print(result_str) - -the program output is: - -.. testoutput:: - - Periodic cubical complex is of dimension 2 - 42 simplices. - -Or it can be defined as follows: - -.. testcode:: - - from gudhi import PeriodicCubicalComplex as pcc - periodic_cc = pcc(dimensions=[3,3], - top_dimensional_cells= [0, 0, 0, 0, 1, 0, 0, 0, 0], - periodic_dimensions=[True, False]) - result_str = 'Periodic cubical complex is of dimension ' + repr(periodic_cc.dimension()) + ' - ' + \ - repr(periodic_cc.num_simplices()) + ' simplices.' - print(result_str) - -the program output is: - -.. testoutput:: - - Periodic cubical complex is of dimension 2 - 42 simplices. - -Examples. ---------- - -End user programs are available in cython/example/ folder. - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/cython/doc/euclidean_strong_witness_complex_ref.rst b/cython/doc/euclidean_strong_witness_complex_ref.rst deleted file mode 100644 index 1a602cd5..00000000 --- a/cython/doc/euclidean_strong_witness_complex_ref.rst +++ /dev/null @@ -1,14 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -================================================= -Euclidean strong witness complex reference manual -================================================= - -.. autoclass:: gudhi.EuclideanStrongWitnessComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.EuclideanStrongWitnessComplex.__init__ diff --git a/cython/doc/euclidean_witness_complex_ref.rst b/cython/doc/euclidean_witness_complex_ref.rst deleted file mode 100644 index 28daf965..00000000 --- a/cython/doc/euclidean_witness_complex_ref.rst +++ /dev/null @@ -1,14 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -========================================== -Euclidean witness complex reference manual -========================================== - -.. autoclass:: gudhi.EuclideanWitnessComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.EuclideanWitnessComplex.__init__ diff --git a/cython/doc/examples.rst b/cython/doc/examples.rst deleted file mode 100644 index 1f02f8a2..00000000 --- a/cython/doc/examples.rst +++ /dev/null @@ -1,29 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Examples -######## - -.. only:: builder_html - - * :download:`rips_complex_from_points_example.py <../example/rips_complex_from_points_example.py>` - * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>` - * :download:`simplex_tree_example.py <../example/simplex_tree_example.py>` - * :download:`alpha_rips_persistence_bottleneck_distance.py <../example/alpha_rips_persistence_bottleneck_distance.py>` - * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` - * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` - * :download:`periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>` - * :download:`bottleneck_basic_example.py <../example/bottleneck_basic_example.py>` - * :download:`gudhi_graphical_tools_example.py <../example/gudhi_graphical_tools_example.py>` - * :download:`witness_complex_from_nearest_landmark_table.py <../example/witness_complex_from_nearest_landmark_table.py>` - * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` - * :download:`rips_complex_diagram_persistence_from_off_file_example.py <../example/rips_complex_diagram_persistence_from_off_file_example.py>` - * :download:`rips_complex_diagram_persistence_from_distance_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py>` - * :download:`rips_persistence_diagram.py <../example/rips_persistence_diagram.py>` - * :download:`random_cubical_complex_persistence_example.py <../example/random_cubical_complex_persistence_example.py>` - * :download:`coordinate_graph_induced_complex.py <../example/coordinate_graph_induced_complex.py>` - * :download:`functional_graph_induced_complex.py <../example/functional_graph_induced_complex.py>` - * :download:`voronoi_graph_induced_complex.py <../example/voronoi_graph_induced_complex.py>` - * :download:`nerve_of_a_covering.py <../example/nerve_of_a_covering.py>` diff --git a/cython/doc/fileformats.rst b/cython/doc/fileformats.rst deleted file mode 100644 index ff20f26e..00000000 --- a/cython/doc/fileformats.rst +++ /dev/null @@ -1,90 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -File formats -############ - -Persistence Diagram -******************* - -Such a file, whose extension is usually ``.pers``, contains a list of -persistence intervals. - -Lines starting with ``#`` are ignored (comments). - -Other lines might contain 2, 3 or 4 values (the number of values on each line -must be the same for all lines):: - - [[field] dimension] birth death - -Here is a simple sample file:: - - # Persistence diagram example - 2 2.7 3.7 - 2 9.6 14. - # Some comments - 3 34.2 34.974 - 4 3. inf - -Other sample files can be found in the `data/persistence_diagram` folder. - -Such files can be generated with -:meth:`gudhi.SimplexTree.write_persistence_diagram`, read with -:meth:`gudhi.read_persistence_intervals_grouped_by_dimension`, or -:meth:`gudhi.read_persistence_intervals_in_dimension` and displayed with -:meth:`gudhi.plot_persistence_barcode` or -:meth:`gudhi.plot_persistence_diagram`. - -Iso-cuboid -********** - -Such a file describes an iso-oriented cuboid with diagonal opposite vertices -(min_x, min_y, min_z,...) and (max_x, max_y, max_z, ...). The format is:: - - min_x min_y [min_z ...] - max_x max_y [max_z ...] - -Here is a simple sample file in the 3D case:: - - -1. -1. -1. - 1. 1. 1. - - -Perseus -******* - -This file format is the format used by the -`Perseus software <http://www.sas.upenn.edu/~vnanda/perseus/>`_ by Vidit Nanda. -The first line contains a number d begin the dimension of the bitmap (2 in the -example below). Next d lines are the numbers of top dimensional cubes in each -dimensions (3 and 3 in the example below). Next, in lexicographical order, the -filtration of top dimensional cubes is given (1 4 6 8 20 4 7 6 5 in the example -below). - -.. figure:: - ../../doc/Bitmap_cubical_complex/exampleBitmap.png - :alt: Example of a input data. - :figclass: align-center - - Example of a input data. - -The input file for the following complex is: - -.. literalinclude:: ../../data/bitmap/cubicalcomplexdoc.txt - -.. centered:: ../../data/bitmap/cubicalcomplexdoc.txt - -To indicate periodic boundary conditions in a given direction, then number of -top dimensional cells in this direction have to be multiplied by -1. For -instance: - -.. literalinclude:: ../../data/bitmap/periodiccubicalcomplexdoc.txt - -.. centered:: ../../data/bitmap/periodiccubicalcomplexdoc.txt - - -Indicate that we have imposed periodic boundary conditions in the direction x, -but not in the direction y. - -Other sample files can be found in the `data/bitmap` folder. diff --git a/cython/doc/img/graphical_tools_representation.png b/cython/doc/img/graphical_tools_representation.png Binary files differdeleted file mode 100644 index 9759f7ba..00000000 --- a/cython/doc/img/graphical_tools_representation.png +++ /dev/null diff --git a/cython/doc/index.rst b/cython/doc/index.rst deleted file mode 100644 index 15cbe267..00000000 --- a/cython/doc/index.rst +++ /dev/null @@ -1,93 +0,0 @@ -GUDHI Python module documentation -################################# - -.. figure:: - ../../doc/common/Gudhi_banner.png - :alt: Gudhi banner - :figclass: align-center - -Introduction -************ - -The Python interface for the Gudhi library (Geometry Understanding in Higher -Dimensions) is a generic open source -`Python module <http://gudhi.gforge.inria.fr/python/latest/>`_, for -Computational Topology and Topological Data Analysis -(`TDA <https://en.wikipedia.org/wiki/Topological_data_analysis>`_). -The GUDHI library intends to help the development of new algorithmic solutions -in TDA and their transfer to applications. It provides robust, efficient, -flexible and easy to use implementations of state-of-the-art algorithms and -data structures. - -The current release of the GUDHI library includes: - -* Data structures to represent, construct and manipulate simplicial complexes. -* Simplification of simplicial complexes by edge contraction. -* Algorithms to compute persistent homology and bottleneck distance. - -We refer to :cite:`gudhilibrary_ICMS14` for a detailed description of the -design of the library. - -Data structures -*************** - -Alpha complex -============= - -.. include:: alpha_complex_sum.inc - -Cover complexes -=============== - -.. include:: nerve_gic_complex_sum.rst - -Cubical complex -=============== - -.. include:: cubical_complex_sum.inc - -Rips complex -============ - -.. include:: rips_complex_sum.inc - -Simplex tree -============ - -.. include:: simplex_tree_sum.inc - -Tangential complex -================== - -.. include:: tangential_complex_sum.inc - -Witness complex -=============== - -.. include:: witness_complex_sum.inc - - -Toolbox -******* - -Bottleneck distance -=================== - -.. include:: bottleneck_distance_sum.inc - -Persistence cohomology -====================== - -.. include:: persistent_cohomology_sum.inc - -Persistence graphical tools -=========================== - -.. include:: persistence_graphical_tools_sum.inc - -Bibliography -************ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/cython/doc/installation.rst b/cython/doc/installation.rst deleted file mode 100644 index 43576ec9..00000000 --- a/cython/doc/installation.rst +++ /dev/null @@ -1,236 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Installation -############ - -Compiling -********* -The library uses c++11 and requires `Boost <https://www.boost.org/>`_ ≥ 1.48.0 -and `CMake <https://www.cmake.org/>`_ ≥ 3.1. -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 <https://wiki.python.org/moin/WindowsCompilers>`_ , 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 -:code:`-DPython_ADDITIONAL_VERSIONS=3` to the cmake command. - -GUDHI Cythonization -=================== - -To build the GUDHI cython module, run the following commands in a terminal: - -.. code-block:: bash - - cd /path-to-gudhi/ - mkdir build - cd build/ - cmake .. - make cython - -Test suites -=========== - -To test your build, `py.test <http://doc.pytest.org>`_ is optional. Run the -following command in a terminal: - -.. code-block:: bash - - 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 - -Debugging issues -================ - -If tests fail, please check your PYTHONPATH and try to :code:`import gudhi` -and check the errors. -The problem can come from a third-party library bad link or installation. - -If :code:`import gudhi` succeeds, please have a look to debug informations: - -.. code-block:: python - - import gudhi - print(gudhi.__debug_info__) - -You shall have something like: - -.. code-block:: none - - Python version 2.7.15 - Cython version 0.26.1 - Eigen3 version 3.1.1 - Installed modules are: off_reader;simplex_tree;rips_complex;cubical_complex;periodic_cubical_complex; - persistence_graphical_tools;reader_utils;witness_complex;strong_witness_complex;alpha_complex; - euclidean_witness_complex;euclidean_strong_witness_complex; - Missing modules are: bottleneck_distance;nerve_gic;subsampling;tangential_complex;persistence_graphical_tools; - 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 numpy and matplotlib are not -available. -Unitary tests cannot be run as pytest is missing. - -A complete configuration would be : - -.. code-block:: none - - 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 <http://http://www.sphinx-doc.org>`_ is -required. Please refer to *conf.py* file to see which -`sphinx-doc <http://http://www.sphinx-doc.org>`_ modules are required to -generate the documentation. Run the following commands in a terminal: - -.. code-block:: bash - - make sphinx - -Optional third-party library -**************************** - -CGAL -==== - -The :doc:`Alpha complex </alpha_complex_user>`, -:doc:`Tangential complex </tangential_complex_user>` and -:doc:`Witness complex </witness_complex_user>` data structures, and -:doc:`Bottleneck distance </bottleneck_distance_user>` 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 <http://doc.cgal.org/latest/Manual/installation.html>`_. - -The following examples requires CGAL version ≥ 4.7.0: - -.. only:: builder_html - - * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` - * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>` - -The following examples requires CGAL version ≥ 4.8.0: - -.. only:: builder_html - - * :download:`bottleneck_basic_example.py <../example/bottleneck_basic_example.py>` - * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` - -The following examples requires CGAL version ≥ 4.8.1: - -.. only:: builder_html - - * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` - -Eigen3 -====== - -The :doc:`Alpha complex </alpha_complex_user>`, -:doc:`Tangential complex </tangential_complex_user>` and -:doc:`Witness complex </witness_complex_user>` data structures and few -examples requires `Eigen3 <http://eigen.tuxfamily.org/>`_, a C++ template -library for linear algebra: matrices, vectors, numerical solvers, and related -algorithms. - -The following examples require the `Eigen3 <http://eigen.tuxfamily.org/>`_: - -.. only:: builder_html - - * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` - * :download:`alpha_complex_from_points_example.py <../example/alpha_complex_from_points_example.py>` - * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` - * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` - -Matplotlib -========== - -The :doc:`persistence graphical tools </persistence_graphical_tools_user>` -module requires `Matplotlib <http://matplotlib.org>`_, 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 <http://matplotlib.org>`_: - -.. only:: builder_html - - * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` - * :download:`gudhi_graphical_tools_example.py <../example/gudhi_graphical_tools_example.py>` - * :download:`periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>` - * :download:`rips_complex_diagram_persistence_from_off_file_example.py <../example/rips_complex_diagram_persistence_from_off_file_example.py>` - * :download:`rips_persistence_diagram.py <../example/rips_persistence_diagram.py>` - * :download:`rips_complex_diagram_persistence_from_distance_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py>` - * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` - * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` - -Numpy -===== - -The :doc:`persistence graphical tools </persistence_graphical_tools_user>` -module requires `NumPy <http://numpy.org>`_, a fundamental package for -scientific computing with Python. - -The following examples require the `NumPy <http://numpy.org>`_: - -.. only:: builder_html - - * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` - * :download:`gudhi_graphical_tools_example.py <../example/gudhi_graphical_tools_example.py>` - * :download:`periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>` - * :download:`rips_complex_diagram_persistence_from_off_file_example.py <../example/rips_complex_diagram_persistence_from_off_file_example.py>` - * :download:`rips_persistence_diagram.py <../example/rips_persistence_diagram.py>` - * :download:`rips_complex_diagram_persistence_from_distance_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py>` - * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` - * :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - * :download:`euclidean_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py>` - -Threading Building Blocks -========================= - -`Intel® TBB <https://www.threadingbuildingblocks.org/>`_ 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. diff --git a/cython/doc/nerve_gic_complex_ref.rst b/cython/doc/nerve_gic_complex_ref.rst deleted file mode 100644 index e24e01fc..00000000 --- a/cython/doc/nerve_gic_complex_ref.rst +++ /dev/null @@ -1,10 +0,0 @@ -================================ -Cover complexes reference manual -================================ - -.. autoclass:: gudhi.CoverComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.CoverComplex.__init__ diff --git a/cython/doc/nerve_gic_complex_sum.rst b/cython/doc/nerve_gic_complex_sum.rst deleted file mode 100644 index 72782c7a..00000000 --- a/cython/doc/nerve_gic_complex_sum.rst +++ /dev/null @@ -1,15 +0,0 @@ -================================================================= =================================== =================================== -:Author: Mathieu Carrière :Introduced in: GUDHI 2.1.0 :Copyright: GPL v3 -:Requires: CGAL :math:`\geq` 4.8.1 -================================================================= =================================== =================================== - -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| .. figure:: | Nerves and Graph Induced Complexes are cover complexes, i.e. | -| ../../doc/Nerve_GIC/gicvisu.jpg | simplicial complexes that provably contain topological information | -| :alt: Graph Induced Complex of a point cloud. | about the input data. They can be computed with a cover of the data, | -| :figclass: align-center | that comes i.e. from the preimage of a family of intervals covering | -| | the image of a scalar-valued function defined on the data. | -| Graph Induced Complex of a point cloud. | | -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| :doc:`nerve_gic_complex_user` | :doc:`nerve_gic_complex_ref` | -+----------------------------------------------------------------+------------------------------------------------------------------------+ diff --git a/cython/doc/nerve_gic_complex_user.rst b/cython/doc/nerve_gic_complex_user.rst deleted file mode 100644 index d774827e..00000000 --- a/cython/doc/nerve_gic_complex_user.rst +++ /dev/null @@ -1,312 +0,0 @@ -Cover complexes user manual -=========================== -Definition ----------- - -.. include:: nerve_gic_complex_sum.rst - -Visualizations of the simplicial complexes can be done with either -neato (from `graphviz <http://www.graphviz.org/>`_), -`geomview <http://www.geomview.org/>`_, -`KeplerMapper <https://github.com/MLWave/kepler-mapper>`_. -Input point clouds are assumed to be -`OFF files <http://www.geomview.org/docs/html/OFF.html>`_. - -Covers ------- - -Nerves and Graph Induced Complexes require a cover C of the input point cloud P, -that is a set of subsets of P whose union is P itself. -Very often, this cover is obtained from the preimage of a family of intervals covering -the image of some scalar-valued function f defined on P. This family is parameterized -by its resolution, which can be either the number or the length of the intervals, -and its gain, which is the overlap percentage between consecutive intervals (ordered by their first values). - -Nerves ------- - -Nerve definition -^^^^^^^^^^^^^^^^ - -Assume you are given a cover C of your point cloud P. Then, the Nerve of this cover -is the simplicial complex that has one k-simplex per k-fold intersection of cover elements. -See also `Wikipedia <https://en.wikipedia.org/wiki/Nerve_of_a_covering>`_. - -.. figure:: - ../../doc/Nerve_GIC/nerve.png - :figclass: align-center - :alt: Nerve of a double torus - - Nerve of a double torus - -Example -^^^^^^^ - -This example builds the Nerve of a point cloud sampled on a 3D human shape (human.off). -The cover C comes from the preimages of intervals (10 intervals with gain 0.3) -covering the height function (coordinate 2), -which are then refined into their connected components using the triangulation of the .OFF file. - -.. testcode:: - - import gudhi - nerve_complex = gudhi.CoverComplex() - nerve_complex.set_verbose(True) - - if (nerve_complex.read_point_cloud(gudhi.__root_source_dir__ + \ - '/data/points/human.off')): - nerve_complex.set_type('Nerve') - nerve_complex.set_color_from_coordinate(2) - nerve_complex.set_function_from_coordinate(2) - nerve_complex.set_graph_from_OFF() - nerve_complex.set_resolution_with_interval_number(10) - nerve_complex.set_gain(0.3) - nerve_complex.set_cover_from_function() - nerve_complex.find_simplices() - nerve_complex.write_info() - simplex_tree = nerve_complex.create_simplex_tree() - nerve_complex.compute_PD() - result_str = 'Nerve is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - for filtered_value in simplex_tree.get_filtration(): - print(filtered_value[0]) - -the program output is: - -.. code-block:: none - - Min function value = -0.979672 and Max function value = 0.816414 - Interval 0 = [-0.979672, -0.761576] - Interval 1 = [-0.838551, -0.581967] - Interval 2 = [-0.658942, -0.402359] - Interval 3 = [-0.479334, -0.22275] - Interval 4 = [-0.299725, -0.0431414] - Interval 5 = [-0.120117, 0.136467] - Interval 6 = [0.059492, 0.316076] - Interval 7 = [0.239101, 0.495684] - Interval 8 = [0.418709, 0.675293] - Interval 9 = [0.598318, 0.816414] - Computing preimages... - Computing connected components... - 5 interval(s) in dimension 0: - [-0.909111, 0.0081753] - [-0.171433, 0.367393] - [-0.171433, 0.367393] - [-0.909111, 0.745853] - 0 interval(s) in dimension 1: - -.. testoutput:: - - Nerve is of dimension 1 - 41 simplices - 21 vertices. - [0] - [1] - [4] - [1, 4] - [2] - [0, 2] - [8] - [2, 8] - [5] - [4, 5] - [9] - [8, 9] - [13] - [5, 13] - [14] - [9, 14] - [19] - [13, 19] - [25] - [32] - [20] - [20, 32] - [33] - [25, 33] - [26] - [14, 26] - [19, 26] - [42] - [26, 42] - [34] - [33, 34] - [27] - [20, 27] - [35] - [27, 35] - [34, 35] - [35, 42] - [44] - [35, 44] - [54] - [44, 54] - - -The program also writes a file ../../data/points/human.off_sc.txt. The first -three lines in this file are the location of the input point cloud and the -function used to compute the cover. -The fourth line contains the number of vertices nv and edges ne of the Nerve. -The next nv lines represent the vertices. Each line contains the vertex ID, -the number of data points it contains, and their average color function value. -Finally, the next ne lines represent the edges, characterized by the ID of -their vertices. - -Using KeplerMapper, one can obtain the following visualization: - -.. figure:: - ../../doc/Nerve_GIC/nervevisu.jpg - :figclass: align-center - :alt: Visualization with KeplerMapper - - Visualization with KeplerMapper - -Graph Induced Complexes (GIC) ------------------------------ - -GIC definition -^^^^^^^^^^^^^^ - -Again, assume you are given a cover C of your point cloud P. Moreover, assume -you are also given a graph G built on top of P. Then, for any clique in G -whose nodes all belong to different elements of C, the GIC includes a -corresponding simplex, whose dimension is the number of nodes in the clique -minus one. -See :cite:`Dey13` for more details. - -.. figure:: - ../../doc/Nerve_GIC/GIC.jpg - :figclass: align-center - :alt: GIC of a point cloud - - GIC of a point cloud - -Example with cover from Voronoï -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -This example builds the GIC of a point cloud sampled on a 3D human shape -(human.off). -We randomly subsampled 100 points in the point cloud, which act as seeds of -a geodesic Voronoï diagram. Each cell of the diagram is then an element of C. -The graph G (used to compute both the geodesics for Voronoï and the GIC) -comes from the triangulation of the human shape. Note that the resulting -simplicial complex is in dimension 3 in this example. - -.. testcode:: - - import gudhi - nerve_complex = gudhi.CoverComplex() - - if (nerve_complex.read_point_cloud(gudhi.__root_source_dir__ + \ - '/data/points/human.off')): - nerve_complex.set_type('GIC') - nerve_complex.set_color_from_coordinate() - nerve_complex.set_graph_from_OFF() - nerve_complex.set_cover_from_Voronoi(700) - nerve_complex.find_simplices() - nerve_complex.plot_off() - -the program outputs SC.off. Using e.g. - -.. code-block:: none - - geomview ../../data/points/human.off_sc.off - -one can obtain the following visualization: - -.. figure:: - ../../doc/Nerve_GIC/gicvoronoivisu.jpg - :figclass: align-center - :alt: Visualization with Geomview - - Visualization with Geomview - -Functional GIC -^^^^^^^^^^^^^^ - -If one restricts to the cliques in G whose nodes all belong to preimages of -consecutive intervals (assuming the cover of the height function is minimal, -i.e. no more than two intervals can intersect at a time), the GIC is of -dimension one, i.e. a graph. -We call this graph the functional GIC. See :cite:`Carriere16` for more details. - -Example -^^^^^^^ - -Functional GIC comes with automatic selection of the Rips threshold, -the resolution and the gain of the function cover. See :cite:`Carriere17c` for -more details. In this example, we compute the functional GIC of a Klein bottle -embedded in R^5, where the graph G comes from a Rips complex with automatic -threshold, and the cover C comes from the preimages of intervals covering the -first coordinate, with automatic resolution and gain. Note that automatic -threshold, resolution and gain can be computed as well for the Nerve. - -.. testcode:: - - import gudhi - nerve_complex = gudhi.CoverComplex() - - if (nerve_complex.read_point_cloud(gudhi.__root_source_dir__ + \ - '/data/points/KleinBottle5D.off')): - nerve_complex.set_type('GIC') - nerve_complex.set_color_from_coordinate(0) - nerve_complex.set_function_from_coordinate(0) - nerve_complex.set_graph_from_automatic_rips() - nerve_complex.set_automatic_resolution() - nerve_complex.set_gain() - nerve_complex.set_cover_from_function() - nerve_complex.find_simplices() - nerve_complex.plot_dot() - -the program outputs SC.dot. Using e.g. - -.. code-block:: none - - neato ../../data/points/KleinBottle5D.off_sc.dot -Tpdf -o ../../data/points/KleinBottle5D.off_sc.pdf - -one can obtain the following visualization: - -.. figure:: - ../../doc/Nerve_GIC/coordGICvisu2.jpg - :figclass: align-center - :alt: Visualization with neato - - Visualization with neato - -where nodes are colored by the filter function values and, for each node, the -first number is its ID and the second is the number of data points that its -contain. - -We also provide an example on a set of 72 pictures taken around the same object -(lucky_cat.off). -The function is now the first eigenfunction given by PCA, whose values are -written in a file (lucky_cat_PCA1). Threshold, resolution and gain are -automatically selected as before. - -.. testcode:: - - import gudhi - nerve_complex = gudhi.CoverComplex() - - if (nerve_complex.read_point_cloud(gudhi.__root_source_dir__ + \ - '/data/points/COIL_database/lucky_cat.off')): - nerve_complex.set_type('GIC') - pca_file = gudhi.__root_source_dir__ + \ - '/data/points/COIL_database/lucky_cat_PCA1' - nerve_complex.set_color_from_file(pca_file) - nerve_complex.set_function_from_file(pca_file) - nerve_complex.set_graph_from_automatic_rips() - nerve_complex.set_automatic_resolution() - nerve_complex.set_gain() - nerve_complex.set_cover_from_function() - nerve_complex.find_simplices() - nerve_complex.plot_dot() - -the program outputs again SC.dot which gives the following visualization after using neato: - -.. figure:: - ../../doc/Nerve_GIC/funcGICvisu.jpg - :figclass: align-center - :alt: Visualization with neato - - Visualization with neato diff --git a/cython/doc/periodic_cubical_complex_ref.rst b/cython/doc/periodic_cubical_complex_ref.rst deleted file mode 100644 index 4b831647..00000000 --- a/cython/doc/periodic_cubical_complex_ref.rst +++ /dev/null @@ -1,13 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Periodic cubical complex reference manual -######################################### - -.. autoclass:: gudhi.PeriodicCubicalComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.PeriodicCubicalComplex.__init__ diff --git a/cython/doc/persistence_graphical_tools_ref.rst b/cython/doc/persistence_graphical_tools_ref.rst deleted file mode 100644 index a2c6bcef..00000000 --- a/cython/doc/persistence_graphical_tools_ref.rst +++ /dev/null @@ -1,11 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -============================================ -Persistence graphical tools reference manual -============================================ - -.. autofunction:: gudhi.__min_birth_max_death -.. autofunction:: gudhi.plot_persistence_barcode -.. autofunction:: gudhi.plot_persistence_diagram diff --git a/cython/doc/persistence_graphical_tools_sum.inc b/cython/doc/persistence_graphical_tools_sum.inc deleted file mode 100644 index d602daa7..00000000 --- a/cython/doc/persistence_graphical_tools_sum.inc +++ /dev/null @@ -1,12 +0,0 @@ -================================================================= =================================== =================================== -:Author: Vincent Rouvreau :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -:Requires: Matplotlib Numpy -================================================================= =================================== =================================== - -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ -| .. figure:: | These graphical tools comes on top of persistence results and allows | -| img/graphical_tools_representation.png | the user to build easily barcode and persistence diagram. | -| | | -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ -| :doc:`persistence_graphical_tools_user` | :doc:`persistence_graphical_tools_ref` | -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ diff --git a/cython/doc/persistence_graphical_tools_user.rst b/cython/doc/persistence_graphical_tools_user.rst deleted file mode 100644 index 292915eb..00000000 --- a/cython/doc/persistence_graphical_tools_user.rst +++ /dev/null @@ -1,62 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Persistence graphical tools user manual -======================================= -Definition ----------- -.. include:: persistence_graphical_tools_sum.inc - - -Show persistence as a barcode ------------------------------ - -This function can display the persistence result as a barcode: - -.. plot:: - :include-source: - - import gudhi - - perseus_file = gudhi.__root_source_dir__ + '/data/bitmap/3d_torus.txt' - periodic_cc = gudhi.PeriodicCubicalComplex(perseus_file=perseus_file) - diag = periodic_cc.persistence() - print("diag = ", diag) - plt = gudhi.plot_persistence_barcode(diag) - plt.show() - -Show persistence as a diagram ------------------------------ - -This function can display the persistence result as a diagram: - -.. plot:: - :include-source: - - import gudhi - - # rips_on_tore3D_1307.pers obtained from write_persistence_diagram method - persistence_file=gudhi.__root_source_dir__ + \ - '/data/persistence_diagram/rips_on_tore3D_1307.pers' - plt = gudhi.plot_persistence_diagram(persistence_file=persistence_file, - legend=True) - plt.show() - -If you want more information on a specific dimension, for instance: - -.. plot:: - :include-source: - - import gudhi - - persistence_file=gudhi.__root_source_dir__ + \ - '/data/persistence_diagram/rips_on_tore3D_1307.pers' - diag = \ - gudhi.read_persistence_intervals_grouped_by_dimension(persistence_file=\ - persistence_file) - dim = 1 - # Display all points with some transparency - plt = gudhi.plot_persistence_diagram([(dim,interval) for interval in diag[dim]], - max_plots=0, alpha=0.1) - plt.show() diff --git a/cython/doc/persistent_cohomology_sum.inc b/cython/doc/persistent_cohomology_sum.inc deleted file mode 100644 index a26df1dc..00000000 --- a/cython/doc/persistent_cohomology_sum.inc +++ /dev/null @@ -1,27 +0,0 @@ -================================================================= =================================== =================================== -:Author: Clément Maria :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -================================================================= =================================== =================================== - -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ -| .. figure:: | The theory of homology consists in attaching to a topological space | -| ../../doc/Persistent_cohomology/3DTorus_poch.png | a sequence of (homology) groups, capturing global topological | -| :figclass: align-center | features like connected components, holes, cavities, etc. Persistent | -| | homology studies the evolution -- birth, life and death -- of these | -| Rips Persistent Cohomology on a 3D | features when the topological space is changing. Consequently, the | -| Torus | theory is essentially composed of three elements: topological spaces, | -| | their homology groups and an evolution scheme. | -| | | -| | Computation of persistent cohomology using the algorithm of | -| | :cite:`DBLP:journals/dcg/SilvaMV11` and | -| | :cite:`DBLP:journals/corr/abs-1208-5018` and the Compressed | -| | Annotation Matrix implementation of | -| | :cite:`DBLP:conf/esa/BoissonnatDM13`. | -| | | -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ -| :doc:`persistent_cohomology_user` | Please refer to each data structure that contains persistence | -| | feature for reference: | -| | | -| | * :doc:`simplex_tree_ref` | -| | * :doc:`cubical_complex_ref` | -| | * :doc:`periodic_cubical_complex_ref` | -+-----------------------------------------------------------------+-----------------------------------------------------------------------+ diff --git a/cython/doc/persistent_cohomology_user.rst b/cython/doc/persistent_cohomology_user.rst deleted file mode 100644 index ce7fc685..00000000 --- a/cython/doc/persistent_cohomology_user.rst +++ /dev/null @@ -1,118 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Persistent cohomology user manual -================================= -Definition ----------- -===================================== ===================================== ===================================== -:Author: Clément Maria :Introduced in: GUDHI PYTHON 2.0.0 :Copyright: GPL v3 -===================================== ===================================== ===================================== - -+---------------------------------------------+----------------------------------------------------------------------+ -| :doc:`persistent_cohomology_user` | Please refer to each data structure that contains persistence | -| | feature for reference: | -| | | -| | * :doc:`simplex_tree_ref` | -+---------------------------------------------+----------------------------------------------------------------------+ - - -Computation of persistent cohomology using the algorithm of :cite:`DBLP:journals/dcg/SilvaMV11` and -:cite:`DBLP:journals/corr/abs-1208-5018` and the Compressed Annotation Matrix implementation of -:cite:`DBLP:conf/esa/BoissonnatDM13`. - -The theory of homology consists in attaching to a topological space a sequence of (homology) groups, capturing global -topological features like connected components, holes, cavities, etc. Persistent homology studies the evolution -- -birth, life and death -- of these features when the topological space is changing. Consequently, the theory is -essentially composed of three elements: - -* topological spaces -* their homology groups -* an evolution scheme. - -Topological Spaces ------------------- - -Topological spaces are represented by simplicial complexes. -Let :math:`V = \{1, \cdots ,|V|\}` be a set of *vertices*. -A *simplex* :math:`\sigma` is a subset of vertices :math:`\sigma \subseteq V`. -A *simplicial complex* :math:`\mathbf{K}` on :math:`V` is a collection of simplices :math:`\{\sigma\}`, -:math:`\sigma \subseteq V`, such that :math:`\tau \subseteq \sigma \in \mathbf{K} \Rightarrow \tau \in \mathbf{K}`. -The dimension :math:`n=|\sigma|-1` of :math:`\sigma` is its number of elements minus 1. -A *filtration* of a simplicial complex is a function :math:`f:\mathbf{K} \rightarrow \mathbb{R}` satisfying -:math:`f(\tau)\leq f(\sigma)` whenever :math:`\tau \subseteq \sigma`. - -Homology --------- - -For a ring :math:`\mathcal{R}`, the group of *n-chains*, denoted :math:`\mathbf{C}_n(\mathbf{K},\mathcal{R})`, of -:math:`\mathbf{K}` is the group of formal sums of n-simplices with :math:`\mathcal{R}` coefficients. The -*boundary operator* is a linear operator -:math:`\partial_n: \mathbf{C}_n(\mathbf{K},\mathcal{R}) \rightarrow \mathbf{C}_{n-1}(\mathbf{K},\mathcal{R})` -such that :math:`\partial_n \sigma = \partial_n [v_0, \cdots , v_n] = \sum_{i=0}^n (-1)^{i}[v_0,\cdots ,\widehat{v_i}, \cdots,v_n]`, -where :math:`\widehat{v_i}` means :math:`v_i` is omitted from the list. The chain groups form a sequence: - -.. math:: - - \cdots \ \ \mathbf{C}_n(\mathbf{K},\mathcal{R}) \xrightarrow{\ \partial_n\ } - \mathbf{C}_{n-1}(\mathbf{K},\mathcal{R}) \xrightarrow{\partial_{n-1}} \cdots \xrightarrow{\ \partial_2 \ } - \mathbf{C}_1(\mathbf{K},\mathcal{R}) \xrightarrow{\ \partial_1 \ } \mathbf{C}_0(\mathbf{K},\mathcal{R}) - -of finitely many groups :math:`\mathbf{C}_n(\mathbf{K},\mathcal{R})` and homomorphisms :math:`\partial_n`, indexed by -the dimension :math:`n \geq 0`. The boundary operators satisfy the property :math:`\partial_n \circ \partial_{n+1}=0` -for every :math:`n > 0` and we define the homology groups: - -.. math:: - - \mathbf{H}_n(\mathbf{K},\mathcal{R}) = \ker \partial_n / \mathrm{im} \ \partial_{n+1} - -We refer to :cite:`Munkres-elementsalgtop1984` for an introduction to homology -theory and to :cite:`DBLP:books/daglib/0025666` for an introduction to persistent homology. - -Indexing Scheme ---------------- - -"Changing" a simplicial complex consists in applying a simplicial map. An *indexing scheme* is a directed graph -together with a traversal order, such that two consecutive nodes in the graph are connected by an arrow (either forward -or backward). -The nodes represent simplicial complexes and the directed edges simplicial maps. - -From the computational point of view, there are two types of indexing schemes of interest in persistent homology: - -* linear ones - :math:`\bullet \longrightarrow \bullet \longrightarrow \cdots \longrightarrow \bullet \longrightarrow \bullet` - in persistent homology :cite:`DBLP:journals/dcg/ZomorodianC05`, -* zigzag ones - :math:`\bullet \longrightarrow \bullet \longleftarrow \cdots \longrightarrow \bullet \longleftarrow \bullet` - in zigzag persistent homology :cite:`DBLP:journals/focm/CarlssonS10`. - -These indexing schemes have a natural left-to-right traversal order, and we describe them with ranges and iterators. -In the current release of the Gudhi library, only the linear case is implemented. - -In the following, we consider the case where the indexing scheme is induced by a filtration. - -Ordering the simplices by increasing filtration values (breaking ties so as a simplex appears after its subsimplices of -same filtration value) provides an indexing scheme. - -Examples --------- - -We provide several example files: run these examples with -h for details on their use. - -.. only:: builder_html - - * :download:`alpha_complex_diagram_persistence_from_off_file_example.py <../example/alpha_complex_diagram_persistence_from_off_file_example.py>` - * :download:`periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py <../example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py>` - * :download:`rips_complex_diagram_persistence_from_off_file_example.py <../example/rips_complex_diagram_persistence_from_off_file_example.py>` - * :download:`rips_persistence_diagram.py <../example/rips_persistence_diagram.py>` - * :download:`rips_complex_diagram_persistence_from_distance_matrix_file_example.py <../example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py>` - * :download:`random_cubical_complex_persistence_example.py <../example/random_cubical_complex_persistence_example.py>` - * :download:`tangential_complex_plain_homology_from_off_file_example.py <../example/tangential_complex_plain_homology_from_off_file_example.py>` - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/cython/doc/python3-sphinx-build.py b/cython/doc/python3-sphinx-build.py deleted file mode 100755 index 84d158cf..00000000 --- a/cython/doc/python3-sphinx-build.py +++ /dev/null @@ -1,11 +0,0 @@ -#!/usr/bin/env python3 - -""" -Emulate sphinx-build for python3 -""" - -from sys import exit, argv -from sphinx import main - -if __name__ == '__main__': - exit(main(argv)) diff --git a/cython/doc/reader_utils_ref.rst b/cython/doc/reader_utils_ref.rst deleted file mode 100644 index f3ecebad..00000000 --- a/cython/doc/reader_utils_ref.rst +++ /dev/null @@ -1,15 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -============================= -Reader utils reference manual -============================= - -.. autofunction:: gudhi.read_off - -.. autofunction:: gudhi.read_lower_triangular_matrix_from_csv_file - -.. autofunction:: gudhi.read_persistence_intervals_grouped_by_dimension - -.. autofunction:: gudhi.read_persistence_intervals_in_dimension diff --git a/cython/doc/rips_complex_ref.rst b/cython/doc/rips_complex_ref.rst deleted file mode 100644 index 22b5616c..00000000 --- a/cython/doc/rips_complex_ref.rst +++ /dev/null @@ -1,14 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -============================= -Rips complex reference manual -============================= - -.. autoclass:: gudhi.RipsComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.RipsComplex.__init__ diff --git a/cython/doc/rips_complex_sum.inc b/cython/doc/rips_complex_sum.inc deleted file mode 100644 index 5616bfa9..00000000 --- a/cython/doc/rips_complex_sum.inc +++ /dev/null @@ -1,17 +0,0 @@ -================================================================= =================================== =================================== -:Author: Clément Maria, Pawel Dlotko, Vincent Rouvreau :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -================================================================= =================================== =================================== - -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| .. figure:: | Rips complex is a simplicial complex constructed from a one skeleton | -| ../../doc/Rips_complex/rips_complex_representation.png | graph. | -| :figclass: align-center | | -| | The filtration value of each edge is computed from a user-given | -| Rips complex representation | distance function and is inserted until a user-given threshold | -| | value. | -| | | -| | This complex can be built from a point cloud and a distance function, | -| | or from a distance matrix. | -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| :doc:`rips_complex_user` | :doc:`rips_complex_ref` | -+----------------------------------------------------------------+------------------------------------------------------------------------+ diff --git a/cython/doc/rips_complex_user.rst b/cython/doc/rips_complex_user.rst deleted file mode 100644 index a8c06cf9..00000000 --- a/cython/doc/rips_complex_user.rst +++ /dev/null @@ -1,316 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Rips complex user manual -========================= -Definition ----------- - -======================================================= ===================================== ===================================== -:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -======================================================= ===================================== ===================================== - -+-------------------------------------------+----------------------------------------------------------------------+ -| :doc:`rips_complex_user` | :doc:`rips_complex_ref` | -+-------------------------------------------+----------------------------------------------------------------------+ - -`Rips complex <https://en.wikipedia.org/wiki/Vietoris%E2%80%93Rips_complex>`_ is a one skeleton graph that allows to -construct a simplicial complex from it. The input can be a point cloud with a given distance function, or a distance -matrix. - -The filtration value of each edge is computed from a user-given distance function, or directly from the distance -matrix. - -All edges that have a filtration value strictly greater than a given threshold value are not inserted into the complex. - -When creating a simplicial complex from this one skeleton graph, Rips inserts the one skeleton graph into the data -structure, and then expands the simplicial complex when required. - -Vertex name correspond to the index of the point in the given range (aka. the point cloud). - -.. figure:: - ../../doc/Rips_complex/rips_complex_representation.png - :align: center - - Rips-complex one skeleton graph representation - -On this example, as edges (4,5), (4,6) and (5,6) are in the complex, simplex (4,5,6) is added with the filtration value -set with :math:`max(filtration(4,5), filtration(4,6), filtration(5,6))`. And so on for simplex (0,1,2,3). - -If the Rips_complex interfaces are not detailed enough for your need, please refer to rips_persistence_step_by_step.cpp -example, where the graph construction over the Simplex_tree is more detailed. - -Point cloud ------------ - -Example from a point cloud -^^^^^^^^^^^^^^^^^^^^^^^^^^ - -This example builds the one skeleton graph from the given points, and max_edge_length value. -Then it creates a :doc:`Simplex_tree <simplex_tree_ref>` with it. - -Finally, it is asked to display information about the simplicial complex. - -.. testcode:: - - import gudhi - rips_complex = gudhi.RipsComplex(points=[[1, 1], [7, 0], [4, 6], [9, 6], [0, 14], [2, 19], [9, 17]], - max_edge_length=12.0) - - simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) - result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - fmt = '%s -> %.2f' - for filtered_value in simplex_tree.get_filtration(): - print(fmt % tuple(filtered_value)) - -When launching (Rips maximal distance between 2 points is 12.0, is expanded -until dimension 1 - one skeleton graph in other words), the output is: - -.. testoutput:: - - Rips complex is of dimension 1 - 18 simplices - 7 vertices. - [0] -> 0.00 - [1] -> 0.00 - [2] -> 0.00 - [3] -> 0.00 - [4] -> 0.00 - [5] -> 0.00 - [6] -> 0.00 - [2, 3] -> 5.00 - [4, 5] -> 5.39 - [0, 2] -> 5.83 - [0, 1] -> 6.08 - [1, 3] -> 6.32 - [1, 2] -> 6.71 - [5, 6] -> 7.28 - [2, 4] -> 8.94 - [0, 3] -> 9.43 - [4, 6] -> 9.49 - [3, 6] -> 11.00 - -Example from OFF file -^^^^^^^^^^^^^^^^^^^^^ - -This example builds the :doc:`Rips_complex <rips_complex_ref>` from the given -points in an OFF file, and max_edge_length value. -Then it creates a :doc:`Simplex_tree <simplex_tree_ref>` with it. - -Finally, it is asked to display information about the Rips complex. - - -.. testcode:: - - import gudhi - point_cloud = gudhi.read_off(off_file=gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off') - rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=12.0) - simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) - result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - fmt = '%s -> %.2f' - for filtered_value in simplex_tree.get_filtration(): - print(fmt % tuple(filtered_value)) - -the program output is: - -.. testoutput:: - - Rips complex is of dimension 1 - 18 simplices - 7 vertices. - [0] -> 0.00 - [1] -> 0.00 - [2] -> 0.00 - [3] -> 0.00 - [4] -> 0.00 - [5] -> 0.00 - [6] -> 0.00 - [2, 3] -> 5.00 - [4, 5] -> 5.39 - [0, 2] -> 5.83 - [0, 1] -> 6.08 - [1, 3] -> 6.32 - [1, 2] -> 6.71 - [5, 6] -> 7.28 - [2, 4] -> 8.94 - [0, 3] -> 9.43 - [4, 6] -> 9.49 - [3, 6] -> 11.00 - -Distance matrix ---------------- - -Example from a distance matrix -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -This example builds the one skeleton graph from the given distance matrix, and max_edge_length value. -Then it creates a :doc:`Simplex_tree <simplex_tree_ref>` with it. - -Finally, it is asked to display information about the simplicial complex. - -.. testcode:: - - import gudhi - rips_complex = gudhi.RipsComplex(distance_matrix=[[], - [6.0827625303], - [5.8309518948, 6.7082039325], - [9.4339811321, 6.3245553203, 5], - [13.0384048104, 15.6524758425, 8.94427191, 12.0415945788], - [18.0277563773, 19.6468827044, 13.152946438, 14.7648230602, 5.3851648071], - [17.88854382, 17.1172427686, 12.0830459736, 11, 9.4868329805, 7.2801098893]], - max_edge_length=12.0) - - simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) - result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - fmt = '%s -> %.2f' - for filtered_value in simplex_tree.get_filtration(): - print(fmt % tuple(filtered_value)) - -When launching (Rips maximal distance between 2 points is 12.0, is expanded -until dimension 1 - one skeleton graph in other words), the output is: - -.. testoutput:: - - Rips complex is of dimension 1 - 18 simplices - 7 vertices. - [0] -> 0.00 - [1] -> 0.00 - [2] -> 0.00 - [3] -> 0.00 - [4] -> 0.00 - [5] -> 0.00 - [6] -> 0.00 - [2, 3] -> 5.00 - [4, 5] -> 5.39 - [0, 2] -> 5.83 - [0, 1] -> 6.08 - [1, 3] -> 6.32 - [1, 2] -> 6.71 - [5, 6] -> 7.28 - [2, 4] -> 8.94 - [0, 3] -> 9.43 - [4, 6] -> 9.49 - [3, 6] -> 11.00 - -Example from csv file -^^^^^^^^^^^^^^^^^^^^^ - -This example builds the :doc:`Rips_complex <rips_complex_ref>` from the given -distance matrix in a csv file, and max_edge_length value. -Then it creates a :doc:`Simplex_tree <simplex_tree_ref>` with it. - -Finally, it is asked to display information about the Rips complex. - - -.. testcode:: - - import gudhi - distance_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=gudhi.__root_source_dir__ + \ - '/data/distance_matrix/full_square_distance_matrix.csv') - rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, max_edge_length=12.0) - simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) - result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - fmt = '%s -> %.2f' - for filtered_value in simplex_tree.get_filtration(): - print(fmt % tuple(filtered_value)) - -the program output is: - -.. testoutput:: - - Rips complex is of dimension 1 - 18 simplices - 7 vertices. - [0] -> 0.00 - [1] -> 0.00 - [2] -> 0.00 - [3] -> 0.00 - [4] -> 0.00 - [5] -> 0.00 - [6] -> 0.00 - [2, 3] -> 5.00 - [4, 5] -> 5.39 - [0, 2] -> 5.83 - [0, 1] -> 6.08 - [1, 3] -> 6.32 - [1, 2] -> 6.71 - [5, 6] -> 7.28 - [2, 4] -> 8.94 - [0, 3] -> 9.43 - [4, 6] -> 9.49 - [3, 6] -> 11.00 - -Correlation matrix ------------------- - -Example from a correlation matrix -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Analogously to the case of distance matrix, Rips complexes can be also constructed based on correlation matrix. -Given a correlation matrix M, comportment-wise 1-M is a distance matrix. -This example builds the one skeleton graph from the given corelation matrix and threshold value. -Then it creates a :doc:`Simplex_tree <simplex_tree_ref>` with it. - -Finally, it is asked to display information about the simplicial complex. - -.. testcode:: - - import gudhi - import numpy as np - - # User defined correlation matrix is: - # |1 0.06 0.23 0.01 0.89| - # |0.06 1 0.74 0.01 0.61| - # |0.23 0.74 1 0.72 0.03| - # |0.01 0.01 0.72 1 0.7 | - # |0.89 0.61 0.03 0.7 1 | - correlation_matrix=np.array([[1., 0.06, 0.23, 0.01, 0.89], - [0.06, 1., 0.74, 0.01, 0.61], - [0.23, 0.74, 1., 0.72, 0.03], - [0.01, 0.01, 0.72, 1., 0.7], - [0.89, 0.61, 0.03, 0.7, 1.]], float) - - distance_matrix = np.ones((correlation_matrix.shape),float) - correlation_matrix - rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, max_edge_length=1.0) - - simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) - result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - fmt = '%s -> %.2f' - for filtered_value in simplex_tree.get_filtration(): - print(fmt % tuple(filtered_value)) - -When launching (Rips maximal distance between 2 points is 12.0, is expanded -until dimension 1 - one skeleton graph in other words), the output is: - -.. testoutput:: - - Rips complex is of dimension 1 - 15 simplices - 5 vertices. - [0] -> 0.00 - [1] -> 0.00 - [2] -> 0.00 - [3] -> 0.00 - [4] -> 0.00 - [0, 4] -> 0.11 - [1, 2] -> 0.26 - [2, 3] -> 0.28 - [3, 4] -> 0.30 - [1, 4] -> 0.39 - [0, 2] -> 0.77 - [0, 1] -> 0.94 - [2, 4] -> 0.97 - [0, 3] -> 0.99 - [1, 3] -> 0.99 - -.. note:: - As persistence diagrams points will be under the diagonal, - bottleneck distance and persistence graphical tool will not work properly, - this is a known issue. diff --git a/cython/doc/simplex_tree_ref.rst b/cython/doc/simplex_tree_ref.rst deleted file mode 100644 index 9eb8c199..00000000 --- a/cython/doc/simplex_tree_ref.rst +++ /dev/null @@ -1,14 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -============================= -Simplex tree reference manual -============================= - -.. autoclass:: gudhi.SimplexTree - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.SimplexTree.__init__ diff --git a/cython/doc/simplex_tree_sum.inc b/cython/doc/simplex_tree_sum.inc deleted file mode 100644 index fb0e54c1..00000000 --- a/cython/doc/simplex_tree_sum.inc +++ /dev/null @@ -1,14 +0,0 @@ -================================================================= =================================== =================================== -:Author: Clément Maria :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -================================================================= =================================== =================================== - -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| .. figure:: | The simplex tree is an efficient and flexible data structure for | -| ../../doc/Simplex_tree/Simplex_tree_representation.png | representing general (filtered) simplicial complexes. | -| :alt: Simplex tree representation | | -| :figclass: align-center | The data structure is described in | -| | :cite:`boissonnatmariasimplextreealgorithmica` | -| Simplex tree representation | | -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| :doc:`simplex_tree_user` | :doc:`simplex_tree_ref` | -+----------------------------------------------------------------+------------------------------------------------------------------------+ diff --git a/cython/doc/simplex_tree_user.rst b/cython/doc/simplex_tree_user.rst deleted file mode 100644 index aebeb29f..00000000 --- a/cython/doc/simplex_tree_user.rst +++ /dev/null @@ -1,72 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Simplex tree user manual -======================== -Definition ----------- - -.. include:: simplex_tree_sum.inc - -A simplicial complex :math:`\mathbf{K}` on a set of vertices :math:`V = \{1, \cdots ,|V|\}` is a collection of -simplices :math:`\{\sigma\}`, :math:`\sigma \subseteq V` such that -:math:`\tau \subseteq \sigma \in \mathbf{K} \rightarrow \tau \in \mathbf{K}`. The dimension :math:`n=|\sigma|-1` of -:math:`\sigma` is its number of elements minus `1`. - -A filtration of a simplicial complex is a function :math:`f:\mathbf{K} \rightarrow \mathbb{R}` satisfying -:math:`f(\tau)\leq f(\sigma)` whenever :math:`\tau \subseteq \sigma`. Ordering the simplices by increasing filtration -values (breaking ties so as a simplex appears after its subsimplices of same filtration value) provides an indexing -scheme. - - -Implementation --------------- - -There are two implementation of complexes. The first on is the Simplex_tree data structure. -The simplex tree is an efficient and flexible data structure for representing general (filtered) simplicial complexes. -The data structure is described in :cite`boissonnatmariasimplextreealgorithmica`. - -The second one is the Hasse_complex. The Hasse complex is a data structure representing explicitly all co-dimension 1 -incidence relations in a complex. It is consequently faster when accessing the boundary of a simplex, but is less -compact and harder to construct from scratch. - -Example -------- - -.. testcode:: - - import gudhi - st = gudhi.SimplexTree() - if st.insert([0, 1]): - print("[0, 1] inserted") - if st.insert([0, 1, 2], filtration=4.0): - print("[0, 1, 2] inserted") - if st.find([0, 1]): - print("[0, 1] found") - result_str = 'num_vertices=' + repr(st.num_vertices()) - print(result_str) - result_str = 'num_simplices=' + repr(st.num_simplices()) - print(result_str) - print("skeleton(2) =") - for sk_value in st.get_skeleton(2): - print(sk_value) - - -The output is: - -.. testoutput:: - - [0, 1] inserted - [0, 1, 2] inserted - [0, 1] found - num_vertices=3 - num_simplices=7 - skeleton(2) = - ([0, 1, 2], 4.0) - ([0, 1], 0.0) - ([0, 2], 4.0) - ([0], 0.0) - ([1, 2], 4.0) - ([1], 0.0) - ([2], 4.0) diff --git a/cython/doc/strong_witness_complex_ref.rst b/cython/doc/strong_witness_complex_ref.rst deleted file mode 100644 index d624d711..00000000 --- a/cython/doc/strong_witness_complex_ref.rst +++ /dev/null @@ -1,14 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -======================================= -Strong witness complex reference manual -======================================= - -.. autoclass:: gudhi.StrongWitnessComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.StrongWitnessComplex.__init__ diff --git a/cython/doc/tangential_complex_ref.rst b/cython/doc/tangential_complex_ref.rst deleted file mode 100644 index cdfda082..00000000 --- a/cython/doc/tangential_complex_ref.rst +++ /dev/null @@ -1,14 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -=================================== -Tangential complex reference manual -=================================== - -.. autoclass:: gudhi.TangentialComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.TangentialComplex.__init__ diff --git a/cython/doc/tangential_complex_sum.inc b/cython/doc/tangential_complex_sum.inc deleted file mode 100644 index 72b4d7ba..00000000 --- a/cython/doc/tangential_complex_sum.inc +++ /dev/null @@ -1,15 +0,0 @@ -================================================================= =================================== =================================== -:Author: Clément Jamin :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -:Requires: CGAL :math:`\geq` 4.8.0 Eigen3 -================================================================= =================================== =================================== - -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| .. figure:: | A Tangential Delaunay complex is a simplicial complex designed to | -| ../../doc/Tangential_complex/tc_examples.png | reconstruct a :math:`k`-dimensional manifold embedded in :math:`d`- | -| :figclass: align-center | dimensional Euclidean space. The input is a point sample coming from | -| | an unknown manifold. The running time depends only linearly on the | -| Tangential complex representation | extrinsic dimension :math:`d` and exponentially on the intrinsic | -| | dimension :math:`k`. | -+----------------------------------------------------------------+------------------------------------------------------------------------+ -| :doc:`tangential_complex_user` | :doc:`tangential_complex_ref` | -+----------------------------------------------------------------+------------------------------------------------------------------------+ diff --git a/cython/doc/tangential_complex_user.rst b/cython/doc/tangential_complex_user.rst deleted file mode 100644 index 5ce69e86..00000000 --- a/cython/doc/tangential_complex_user.rst +++ /dev/null @@ -1,201 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Tangential complex user manual -============================== -.. include:: tangential_complex_sum.inc - -Definition ----------- - -A Tangential Delaunay complex is a simplicial complex designed to reconstruct a -:math:`k`-dimensional smooth manifold embedded in :math:`d`-dimensional -Euclidean space. The input is a point sample coming from an unknown manifold, -which means that the points lie close to a structure of "small" intrinsic -dimension. The running time depends only linearly on the extrinsic dimension -:math:`d` and exponentially on the intrinsic dimension :math:`k`. - -An extensive description of the Tangential complex can be found in -:cite:`tangentialcomplex2014`. - -What is a Tangential Complex? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Let us start with the description of the Tangential complex of a simple -example, with :math:`k = 1` and :math:`d = 2`. The input data is 4 points -:math:`P` located on a curve embedded in 2D. - -.. figure:: ../../doc/Tangential_complex/tc_example_01.png - :alt: The input - :figclass: align-center - - The input - -For each point :math:`p`, estimate its tangent subspace :math:`T_p` (e.g. -using PCA). - -.. figure:: ../../doc/Tangential_complex/tc_example_02.png - :alt: The estimated normals - :figclass: align-center - - The estimated normals - - -Let us add the Voronoi diagram of the points in orange. For each point -:math:`p`, construct its star in the Delaunay triangulation of :math:`P` -restricted to :math:`T_p`. - -.. figure:: ../../doc/Tangential_complex/tc_example_03.png - :alt: The Voronoi diagram - :figclass: align-center - - The Voronoi diagram - -The Tangential Delaunay complex is the union of those stars. - -In practice, neither the ambient Voronoi diagram nor the ambient Delaunay -triangulation is computed. Instead, local :math:`k`-dimensional regular -triangulations are computed with a limited number of points as we only need the -star of each point. More details can be found in :cite:`tangentialcomplex2014`. - -Inconsistencies -^^^^^^^^^^^^^^^ -Inconsistencies between the stars can occur. An inconsistency occurs when a -simplex is not in the star of all its vertices. - -Let us take the same example. - -.. figure:: ../../doc/Tangential_complex/tc_example_07_before.png - :alt: Before - :figclass: align-center - - Before - -Let us slightly move the tangent subspace :math:`T_q` - -.. figure:: ../../doc/Tangential_complex/tc_example_07_after.png - :alt: After - :figclass: align-center - - After - -Now, the star of :math:`Q` contains :math:`QP`, but the star of :math:`P` does -not contain :math:`QP`. We have an inconsistency. - -.. figure:: ../../doc/Tangential_complex/tc_example_08.png - :alt: After - :figclass: align-center - - After - -One way to solve inconsistencies is to randomly perturb the positions of the -points involved in an inconsistency. In the current implementation, this -perturbation is done in the tangent subspace of each point. The maximum -perturbation radius is given as a parameter to the constructor. - -In most cases, we recommend to provide a point set where the minimum distance -between any two points is not too small. This can be achieved using the -functions provided by the Subsampling module. Then, a good value to start with -for the maximum perturbation radius would be around half the minimum distance -between any two points. The Example with perturbation below shows an example of -such a process. - -In most cases, this process is able to dramatically reduce the number of -inconsistencies, but is not guaranteed to succeed. - -Output -^^^^^^ -The result of the computation is exported as a Simplex_tree. It is the union of -the stars of all the input points. A vertex in the Simplex Tree is the index of -the point in the range provided by the user. The point corresponding to a -vertex can also be obtained through the Tangential_complex::get_point function. -Note that even if the positions of the points are perturbed, their original -positions are kept (e.g. Tangential_complex::get_point returns the original -position of the point). - -The result can be obtained after the computation of the Tangential complex -itself and/or after the perturbation process. - - -Simple example --------------- - -This example builds the Tangential complex of point set read in an OFF file. - -.. testcode:: - - import gudhi - tc = gudhi.TangentialComplex(intrisic_dim = 1, - off_file=gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off') - result_str = 'Tangential contains ' + repr(tc.num_simplices()) + \ - ' simplices - ' + repr(tc.num_vertices()) + ' vertices.' - print(result_str) - - st = tc.create_simplex_tree() - result_str = 'Simplex tree is of dimension ' + repr(st.dimension()) + \ - ' - ' + repr(st.num_simplices()) + ' simplices - ' + \ - repr(st.num_vertices()) + ' vertices.' - print(result_str) - for filtered_value in st.get_filtration(): - print(filtered_value[0]) - -The output is: - -.. testoutput:: - - Tangential contains 12 simplices - 7 vertices. - Simplex tree is of dimension 1 - 15 simplices - 7 vertices. - [0] - [1] - [0, 1] - [2] - [0, 2] - [1, 2] - [3] - [1, 3] - [4] - [2, 4] - [5] - [4, 5] - [6] - [3, 6] - [5, 6] - - -Example with perturbation -------------------------- - -This example builds the Tangential complex of a point set, then tries to solve -inconsistencies by perturbing the positions of points involved in inconsistent -simplices. - -.. testcode:: - - import gudhi - tc = gudhi.TangentialComplex(intrisic_dim = 1, - points=[[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]) - result_str = 'Tangential contains ' + repr(tc.num_vertices()) + ' vertices.' - print(result_str) - - if tc.num_inconsistent_simplices() > 0: - print('Tangential contains inconsistencies.') - - tc.fix_inconsistencies_using_perturbation(10, 60) - if tc.num_inconsistent_simplices() == 0: - print('Inconsistencies has been fixed.') - -The output is: - -.. testoutput:: - - Tangential contains 4 vertices. - Inconsistencies has been fixed. - - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/cython/doc/todos.rst b/cython/doc/todos.rst deleted file mode 100644 index ca274ced..00000000 --- a/cython/doc/todos.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -========== -To be done -========== - -.. todolist:: diff --git a/cython/doc/witness_complex_ref.rst b/cython/doc/witness_complex_ref.rst deleted file mode 100644 index 9987d3fd..00000000 --- a/cython/doc/witness_complex_ref.rst +++ /dev/null @@ -1,14 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -================================ -Witness complex reference manual -================================ - -.. autoclass:: gudhi.WitnessComplex - :members: - :undoc-members: - :show-inheritance: - - .. automethod:: gudhi.WitnessComplex.__init__ diff --git a/cython/doc/witness_complex_sum.inc b/cython/doc/witness_complex_sum.inc deleted file mode 100644 index a8a126a0..00000000 --- a/cython/doc/witness_complex_sum.inc +++ /dev/null @@ -1,19 +0,0 @@ -================================================================= =================================== =================================== -:Author: Siargey Kachanovich :Introduced in: GUDHI 2.0.0 :Copyright: GPL v3 -:Euclidean version requires: CGAL :math:`\geq` 4.6.0 Eigen3 -================================================================= =================================== =================================== - -+-------------------------------------------------------------------+----------------------------------------------------------------------+ -| .. figure:: | Witness complex :math:`Wit(W,L)` is a simplicial complex defined on | -| ../../doc/Witness_complex/Witness_complex_representation.png | two sets of points in :math:`\mathbb{R}^D`. | -| :alt: Witness complex representation | | -| :figclass: align-center | The data structure is described in | -| | :cite:`boissonnatmariasimplextreealgorithmica`. | -| | | -| Witness complex representation | | -+-------------------------------------------------------------------+----------------------------------------------------------------------+ -| :doc:`witness_complex_user` | * :doc:`witness_complex_ref` | -| | * :doc:`strong_witness_complex_ref` | -| | * :doc:`euclidean_witness_complex_ref` | -| | * :doc:`euclidean_strong_witness_complex_ref` | -+-------------------------------------------------------------------+----------------------------------------------------------------------+ diff --git a/cython/doc/witness_complex_user.rst b/cython/doc/witness_complex_user.rst deleted file mode 100644 index 40e94134..00000000 --- a/cython/doc/witness_complex_user.rst +++ /dev/null @@ -1,135 +0,0 @@ -:orphan: - -.. To get rid of WARNING: document isn't included in any toctree - -Witness complex user manual -=========================== - -.. include:: witness_complex_sum.inc - -Definitions ------------ - -Witness complex is a simplicial complex defined on two sets of points in :math:`\mathbb{R}^D`: - -- :math:`W` set of **witnesses** and -- :math:`L` set of **landmarks**. - -Even though often the set of landmarks :math:`L` is a subset of the set of witnesses :math:`W`, it is not a requirement -for the current implementation. - -Landmarks are the vertices of the simplicial complex and witnesses help to decide on which simplices are inserted via a -predicate "is witnessed". - -De Silva and Carlsson in their paper :cite:`de2004topological` differentiate **weak witnessing** and -**strong witnessing**: - -- *weak*: :math:`\sigma \subset L` is witnessed by :math:`w \in W` if :math:`\forall l \in \sigma,\ \forall l' \in \mathbf{L \setminus \sigma},\ d(w,l) \leq d(w,l')` -- *strong*: :math:`\sigma \subset L` is witnessed by :math:`w \in W` if :math:`\forall l \in \sigma,\ \forall l' \in \mathbf{L},\ d(w,l) \leq d(w,l')` - -where :math:`d(.,.)` is a distance function. - -Both definitions can be relaxed by a real value :math:`\alpha`: - -- *weak*: :math:`\sigma \subset L` is :math:`\alpha`-witnessed by :math:`w \in W` if :math:`\forall l \in \sigma,\ \forall l' \in \mathbf{L \setminus \sigma},\ d(w,l)^2 \leq d(w,l')^2 + \alpha^2` -- *strong*: :math:`\sigma \subset L` is :math:`\alpha`-witnessed by :math:`w \in W` if :math:`\forall l \in \sigma,\ \forall l' \in \mathbf{L},\ d(w,l)^2 \leq d(w,l')^2 + \alpha^2` - -which leads to definitions of **weak relaxed witness complex** (or just relaxed witness complex for short) and -**strong relaxed witness complex** respectively. - -.. figure:: ../../doc/Witness_complex/swit.svg - :alt: Strongly witnessed simplex - :figclass: align-center - - Strongly witnessed simplex - - -In particular case of 0-relaxation, weak complex corresponds to **witness complex** introduced in -:cite:`de2004topological`, whereas 0-relaxed strong witness complex consists of just vertices and is not very -interesting. Hence for small relaxation weak version is preferable. -However, to capture the homotopy type (for example using Gudhi::persistent_cohomology::Persistent_cohomology) it is -often necessary to work with higher filtration values. In this case strong relaxed witness complex is faster to compute -and offers similar results. - -Implementation --------------- - -The two complexes described above are implemented in the corresponding classes - -- :doc:`witness_complex_ref` -- :doc:`strong_witness_complex_ref` -- :doc:`euclidean_witness_complex_ref` -- :doc:`euclidean_strong_witness_complex_ref` - -The construction of the Euclidean versions of complexes follow the same scheme: - -1. Construct a search tree on landmarks. -2. Construct lists of nearest landmarks for each witness. -3. Construct the witness complex for nearest landmark lists. - -In the non-Euclidean classes, the lists of nearest landmarks are supposed to be given as input. - -The constructors take on the steps 1 and 2, while the function 'create_complex' executes the step 3. - -Constructing weak relaxed witness complex from an off file ----------------------------------------------------------- - -Let's start with a simple example, which reads an off point file and computes a weak witness complex. - -.. code-block:: python - - import gudhi - import argparse - - parser = argparse.ArgumentParser(description='EuclideanWitnessComplex creation from ' - 'points read in a OFF file.', - epilog='Example: ' - 'example/witness_complex_diagram_persistence_from_off_file_example.py ' - '-f ../data/points/tore3D_300.off -a 1.0 -n 20 -d 2' - '- Constructs a alpha complex with the ' - 'points from the given OFF file.') - parser.add_argument("-f", "--file", type=str, required=True) - parser.add_argument("-a", "--max_alpha_square", type=float, required=True) - parser.add_argument("-n", "--number_of_landmarks", type=int, required=True) - parser.add_argument("-d", "--limit_dimension", type=int, required=True) - - args = parser.parse_args() - - with open(args.file, 'r') as f: - first_line = f.readline() - if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): - print("#####################################################################") - print("EuclideanWitnessComplex creation from points read in a OFF file") - - witnesses = gudhi.read_off(off_file=args.file) - landmarks = gudhi.pick_n_random_points(points=witnesses, nb_points=args.number_of_landmarks) - - message = "EuclideanWitnessComplex with max_edge_length=" + repr(args.max_alpha_square) + \ - " - Number of landmarks=" + repr(args.number_of_landmarks) - print(message) - - witness_complex = gudhi.EuclideanWitnessComplex(witnesses=witnesses, landmarks=landmarks) - simplex_tree = witness_complex.create_simplex_tree(max_alpha_square=args.max_alpha_square, - limit_dimension=args.limit_dimension) - - message = "Number of simplices=" + repr(simplex_tree.num_simplices()) - print(message) - else: - print(args.file, "is not a valid OFF file") - - f.close() - - -Example2: Computing persistence using strong relaxed witness complex --------------------------------------------------------------------- - -Here is an example of constructing a strong witness complex filtration and computing persistence on it: - -* :download:`euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py <../example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py>` - -Bibliography -============ - -.. bibliography:: ../../biblio/bibliography.bib - :filter: docnames - :style: unsrt diff --git a/cython/example/alpha_complex_diagram_persistence_from_off_file_example.py b/cython/example/alpha_complex_diagram_persistence_from_off_file_example.py deleted file mode 100755 index 4abe22d4..00000000 --- a/cython/example/alpha_complex_diagram_persistence_from_off_file_example.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='AlphaComplex creation from ' - 'points read in a OFF file.', - epilog='Example: ' - 'example/alpha_complex_diagram_persistence_from_off_file_example.py ' - '-f ../data/points/tore3D_300.off -a 0.6' - '- Constructs a alpha complex with the ' - 'points from the given OFF file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-a", "--max_alpha_square", type=float, default=0.5) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -with open(args.file, 'r') as f: - first_line = f.readline() - if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): - print("#####################################################################") - print("AlphaComplex creation from points read in a OFF file") - - message = "AlphaComplex with max_edge_length=" + repr(args.max_alpha_square) - print(message) - - alpha_complex = gudhi.AlphaComplex(off_file=args.file) - simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=args.max_alpha_square) - - message = "Number of simplices=" + repr(simplex_tree.num_simplices()) - print(message) - - diag = simplex_tree.persistence() - - print("betti_numbers()=") - print(simplex_tree.betti_numbers()) - - if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(diag, band=args.band) - pplot.show() - else: - print(args.file, "is not a valid OFF file") - - f.close() diff --git a/cython/example/alpha_complex_from_points_example.py b/cython/example/alpha_complex_from_points_example.py deleted file mode 100755 index ad73c744..00000000 --- a/cython/example/alpha_complex_from_points_example.py +++ /dev/null @@ -1,67 +0,0 @@ -#!/usr/bin/env python - -from gudhi import AlphaComplex, SimplexTree - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -print("#####################################################################") -print("AlphaComplex creation from points") -alpha_complex = AlphaComplex(points=[[0, 0], [1, 0], [0, 1], [1, 1]]) -simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=60.0) - -if simplex_tree.find([0, 1]): - print("[0, 1] Found !!") -else: - print("[0, 1] Not found...") - -if simplex_tree.find([4]): - print("[4] Found !!") -else: - print("[4] Not found...") - -if simplex_tree.insert([0, 1, 2], filtration=4.0): - print("[0, 1, 2] Inserted !!") -else: - print("[0, 1, 2] Not inserted...") - -if simplex_tree.insert([0, 1, 4], filtration=4.0): - print("[0, 1, 4] Inserted !!") -else: - print("[0, 1, 4] Not inserted...") - -if simplex_tree.find([4]): - print("[4] Found !!") -else: - print("[4] Not found...") - -print("dimension=", simplex_tree.dimension()) -print("filtrations=", simplex_tree.get_filtration()) -print("star([0])=", simplex_tree.get_star([0])) -print("coface([0], 1)=", simplex_tree.get_cofaces([0], 1)) - -print("point[0]=", alpha_complex.get_point(0)) -print("point[5]=", alpha_complex.get_point(5)) diff --git a/cython/example/alpha_rips_persistence_bottleneck_distance.py b/cython/example/alpha_rips_persistence_bottleneck_distance.py deleted file mode 100755 index b51fa7a8..00000000 --- a/cython/example/alpha_rips_persistence_bottleneck_distance.py +++ /dev/null @@ -1,101 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse -import math - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='AlphaComplex and RipsComplex ' - 'persistence creation from points read in ' - 'a OFF file. Bottleneck distance computation' - ' on each dimension', - epilog='Example: ' - 'example/alpha_rips_persistence_bottleneck_distance.py ' - '-f ../data/points/tore3D_1307.off -t 0.15 -d 3') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-t", "--threshold", type=float, default=0.5) -parser.add_argument("-d", "--max_dimension", type=int, default=1) - -args = parser.parse_args() -with open(args.file, 'r') as f: - first_line = f.readline() - if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): - point_cloud = gudhi.read_off(off_file=args.file) - print("#####################################################################") - print("RipsComplex creation from points read in a OFF file") - - message = "RipsComplex with max_edge_length=" + repr(args.threshold) - print(message) - - rips_complex = gudhi.RipsComplex(points=point_cloud, - max_edge_length=args.threshold) - - rips_stree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) - - message = "Number of simplices=" + repr(rips_stree.num_simplices()) - print(message) - - rips_diag = rips_stree.persistence() - - print("#####################################################################") - print("AlphaComplex creation from points read in a OFF file") - - message = "AlphaComplex with max_edge_length=" + repr(args.threshold) - print(message) - - alpha_complex = gudhi.AlphaComplex(points=point_cloud) - alpha_stree = alpha_complex.create_simplex_tree(max_alpha_square=(args.threshold * args.threshold)) - - message = "Number of simplices=" + repr(alpha_stree.num_simplices()) - print(message) - - alpha_diag = alpha_stree.persistence() - - max_b_distance = 0.0 - for dim in range(args.max_dimension): - # Alpha persistence values needs to be transform because filtration - # values are alpha square values - funcs = [math.sqrt, math.sqrt] - alpha_intervals = [] - for interval in alpha_stree.persistence_intervals_in_dimension(dim): - alpha_intervals.append(map(lambda func,value: func(value), funcs, interval)) - - rips_intervals = rips_stree.persistence_intervals_in_dimension(dim) - bottleneck_distance = gudhi.bottleneck_distance(rips_intervals, alpha_intervals) - message = "In dimension " + repr(dim) + ", bottleneck distance = " + repr(bottleneck_distance) - print(message) - max_b_distance = max(bottleneck_distance, max_b_distance) - - print("================================================================================") - message = "Bottleneck distance is " + repr(max_b_distance) - print(message) - - else: - print(args.file, "is not a valid OFF file") - - f.close() diff --git a/cython/example/bottleneck_basic_example.py b/cython/example/bottleneck_basic_example.py deleted file mode 100755 index 287956e7..00000000 --- a/cython/example/bottleneck_basic_example.py +++ /dev/null @@ -1,46 +0,0 @@ -#!/usr/bin/env python - -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Francois Godi, Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Francois Godi, Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -diag1 = [[2.7, 3.7],[9.6, 14.],[34.2, 34.974], [3.,float('Inf')]] - -diag2 = [[2.8, 4.45],[9.5, 14.1],[3.2,float('Inf')]] - -message = "diag1=" + repr(diag1) -print(message) - -message = "diag2=" + repr(diag2) -print(message) - -message = "Bottleneck distance approximation=" + repr(gudhi.bottleneck_distance(diag1, diag2, 0.1)) -print(message) - -message = "Bottleneck distance exact value=" + repr(gudhi.bottleneck_distance(diag1, diag2)) -print(message) - diff --git a/cython/example/coordinate_graph_induced_complex.py b/cython/example/coordinate_graph_induced_complex.py deleted file mode 100755 index 9e93109a..00000000 --- a/cython/example/coordinate_graph_induced_complex.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2018 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2018 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='Coordinate GIC ' - 'from points read in a OFF file.', - epilog='Example: ' - 'example/coordinate_graph_induced_complex.py ' - '-f ../data/points/KleinBottle5D.off -c 0 -v' - '- Constructs the coordinate GIC with the ' - 'points from the given OFF file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-c", "--coordinate", type=int, default=0) -parser.add_argument("-v", "--verbose", default=False, action='store_true' , help='Flag for program verbosity') - -args = parser.parse_args() - -nerve_complex = gudhi.CoverComplex() -nerve_complex.set_verbose(args.verbose) - -if (nerve_complex.read_point_cloud(args.file)): - nerve_complex.set_type('GIC') - nerve_complex.set_color_from_coordinate(args.coordinate) - nerve_complex.set_function_from_coordinate(args.coordinate) - nerve_complex.set_graph_from_automatic_rips() - nerve_complex.set_automatic_resolution() - nerve_complex.set_gain() - nerve_complex.set_cover_from_function() - nerve_complex.find_simplices() - nerve_complex.plot_dot() - simplex_tree = nerve_complex.create_simplex_tree() - nerve_complex.compute_PD() - if (args.verbose): - print('Iterator on coordinate GIC simplices') - result_str = 'Coordinate GIC is of dimension ' + \ - repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - for filtered_value in simplex_tree.get_filtration(): - print(filtered_value[0]) diff --git a/cython/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py b/cython/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py deleted file mode 100755 index 3b29781f..00000000 --- a/cython/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='EuclideanStrongWitnessComplex creation from ' - 'points read in a OFF file.', - epilog='Example: ' - 'example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py ' - '-f ../data/points/tore3D_300.off -a 1.0 -n 20 -d 2' - '- Constructs a strong witness complex with the ' - 'points from the given OFF file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-a", "--max_alpha_square", type=float, required=True) -parser.add_argument("-n", "--number_of_landmarks", type=int, required=True) -parser.add_argument("-d", "--limit_dimension", type=int, required=True) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -with open(args.file, 'r') as f: - first_line = f.readline() - if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): - print("#####################################################################") - print("EuclideanStrongWitnessComplex creation from points read in a OFF file") - - witnesses = gudhi.read_off(off_file=args.file) - landmarks = gudhi.pick_n_random_points(points=witnesses, nb_points=args.number_of_landmarks) - - message = "EuclideanStrongWitnessComplex with max_edge_length=" + repr(args.max_alpha_square) + \ - " - Number of landmarks=" + repr(args.number_of_landmarks) - print(message) - - witness_complex = gudhi.EuclideanStrongWitnessComplex(witnesses=witnesses, landmarks=landmarks) - simplex_tree = witness_complex.create_simplex_tree(max_alpha_square=args.max_alpha_square, - limit_dimension=args.limit_dimension) - - message = "Number of simplices=" + repr(simplex_tree.num_simplices()) - print(message) - - diag = simplex_tree.persistence() - - print("betti_numbers()=") - print(simplex_tree.betti_numbers()) - - if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(diag, band=args.band) - pplot.show() - else: - print(args.file, "is not a valid OFF file") - - f.close() diff --git a/cython/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py b/cython/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py deleted file mode 100755 index db34962d..00000000 --- a/cython/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='EuclideanWitnessComplex creation from ' - 'points read in a OFF file.', - epilog='Example: ' - 'example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py ' - '-f ../data/points/tore3D_300.off -a 1.0 -n 20 -d 2' - '- Constructs a weak witness complex with the ' - 'points from the given OFF file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-a", "--max_alpha_square", type=float, required=True) -parser.add_argument("-n", "--number_of_landmarks", type=int, required=True) -parser.add_argument("-d", "--limit_dimension", type=int, required=True) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -with open(args.file, 'r') as f: - first_line = f.readline() - if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): - print("#####################################################################") - print("EuclideanWitnessComplex creation from points read in a OFF file") - - witnesses = gudhi.read_off(off_file=args.file) - landmarks = gudhi.pick_n_random_points(points=witnesses, nb_points=args.number_of_landmarks) - - message = "EuclideanWitnessComplex with max_edge_length=" + repr(args.max_alpha_square) + \ - " - Number of landmarks=" + repr(args.number_of_landmarks) - print(message) - - witness_complex = gudhi.EuclideanWitnessComplex(witnesses=witnesses, landmarks=landmarks) - simplex_tree = witness_complex.create_simplex_tree(max_alpha_square=args.max_alpha_square, - limit_dimension=args.limit_dimension) - - message = "Number of simplices=" + repr(simplex_tree.num_simplices()) - print(message) - - diag = simplex_tree.persistence() - - print("betti_numbers()=") - print(simplex_tree.betti_numbers()) - - if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(diag, band=args.band) - pplot.show() - else: - print(args.file, "is not a valid OFF file") - - f.close() diff --git a/cython/example/functional_graph_induced_complex.py b/cython/example/functional_graph_induced_complex.py deleted file mode 100755 index 6ad7c2ec..00000000 --- a/cython/example/functional_graph_induced_complex.py +++ /dev/null @@ -1,69 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2018 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2018 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='Functional GIC ' - 'from points read in a OFF file.', - epilog='Example: ' - 'example/functional_graph_induced_complex.py ' - '-o ../data/points/COIL_database/lucky_cat.off ' - '-f ../data/points/COIL_database/lucky_cat_PCA1' - '- Constructs the functional GIC with the ' - 'points from the given OFF and function files.') -parser.add_argument("-o", "--off-file", type=str, required=True) -parser.add_argument("-f", "--function-file", type=str, required=True) -parser.add_argument("-v", "--verbose", default=False, action='store_true' , help='Flag for program verbosity') - -args = parser.parse_args() - -nerve_complex = gudhi.CoverComplex() -nerve_complex.set_verbose(args.verbose) - -if (nerve_complex.read_point_cloud(args.off_file)): - nerve_complex.set_type('GIC') - nerve_complex.set_color_from_file(args.function_file) - nerve_complex.set_function_from_file(args.function_file) - nerve_complex.set_graph_from_automatic_rips() - nerve_complex.set_automatic_resolution() - nerve_complex.set_gain() - nerve_complex.set_cover_from_function() - nerve_complex.find_simplices() - nerve_complex.plot_dot() - simplex_tree = nerve_complex.create_simplex_tree() - nerve_complex.compute_PD() - if (args.verbose): - print('Iterator on functional GIC simplices') - result_str = 'Functional GIC is of dimension ' + \ - repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - for filtered_value in simplex_tree.get_filtration(): - print(filtered_value[0]) diff --git a/cython/example/gudhi_graphical_tools_example.py b/cython/example/gudhi_graphical_tools_example.py deleted file mode 100755 index ac3d146c..00000000 --- a/cython/example/gudhi_graphical_tools_example.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python - -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -print("#####################################################################") -print("Show barcode persistence example") - -persistence = [(2, (1.0, float('inf'))), (1, (1.4142135623730951, float('inf'))), - (1, (1.4142135623730951, float('inf'))), (0, (0.0, float('inf'))), - (0, (0.0, 1.0)), (0, (0.0, 1.0)), (0, (0.0, 1.0))] -gudhi.plot_persistence_barcode(persistence) - -print("#####################################################################") -print("Show diagram persistence example") - -pplot = gudhi.plot_persistence_diagram(persistence) -pplot.show() - -print("#####################################################################") -print("Show diagram persistence example with a confidence band") - -pplot = gudhi.plot_persistence_diagram(persistence, band=0.2) -pplot.show() diff --git a/cython/example/nerve_of_a_covering.py b/cython/example/nerve_of_a_covering.py deleted file mode 100755 index c5577cb1..00000000 --- a/cython/example/nerve_of_a_covering.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2018 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2018 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='Nerve of a covering creation ' - 'from points read in a OFF file.', - epilog='Example: ' - 'example/nerve_of_a_covering.py ' - '-f ../data/points/human.off -c 2 -r 10 -g 0.3' - '- Constructs Nerve of a covering with the ' - 'points from the given OFF file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-c", "--coordinate", type=int, default=0) -parser.add_argument("-r", "--resolution", type=int, default=10) -parser.add_argument("-g", "--gain", type=float, default=0.3) -parser.add_argument("-v", "--verbose", default=False, action='store_true' , help='Flag for program verbosity') - -args = parser.parse_args() - -nerve_complex = gudhi.CoverComplex() -nerve_complex.set_verbose(args.verbose) - -if (nerve_complex.read_point_cloud(args.file)): - nerve_complex.set_type('Nerve') - nerve_complex.set_color_from_coordinate(args.coordinate) - nerve_complex.set_function_from_coordinate(args.coordinate) - nerve_complex.set_graph_from_OFF() - nerve_complex.set_resolution_with_interval_number(args.resolution) - nerve_complex.set_gain(args.gain) - nerve_complex.set_cover_from_function() - nerve_complex.find_simplices() - nerve_complex.write_info() - simplex_tree = nerve_complex.create_simplex_tree() - nerve_complex.compute_PD() - if (args.verbose): - print('Iterator on graph induced complex simplices') - result_str = 'Nerve is of dimension ' + \ - repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - for filtered_value in simplex_tree.get_filtration(): - print(filtered_value[0]) diff --git a/cython/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py b/cython/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py deleted file mode 100755 index 5f968bf1..00000000 --- a/cython/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py +++ /dev/null @@ -1,76 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -def is_file_perseus(file): - num_lines = open(file).read().count('\n') - try: - f = open(file) - num_dim = int(f.readline()) - coeff = 1 - for dim in range(0, num_dim): - try: - line = int(f.readline()) - coeff *= abs(line) - except ValueError: - return False - if num_lines == (1 + num_dim + coeff): - return True - else: - return False - except ValueError: - return False - -parser = argparse.ArgumentParser(description='Periodic cubical complex from a ' - 'Perseus-style file name.', - epilog='Example: ' - './periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py' - ' -f ../data/bitmap/CubicalTwoSphere.txt') - -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument('--no-barcode', default=False, action='store_true' , help='Flag for not to display the barcodes') - -args = parser.parse_args() - -if is_file_perseus(args.file): - print("#####################################################################") - print("PeriodicCubicalComplex creation") - periodic_cubical_complex = gudhi.PeriodicCubicalComplex(perseus_file=args.file) - - print("persistence(homology_coeff_field=3, min_persistence=0)=") - diag = periodic_cubical_complex.persistence(homology_coeff_field=3, min_persistence=0) - print(diag) - - print("betti_numbers()=") - print(periodic_cubical_complex.betti_numbers()) - if args.no_barcode == False: - gudhi.plot_persistence_barcode(diag) -else: - print(args.file, "is not a valid perseus style file") diff --git a/cython/example/random_cubical_complex_persistence_example.py b/cython/example/random_cubical_complex_persistence_example.py deleted file mode 100755 index 80ff2452..00000000 --- a/cython/example/random_cubical_complex_persistence_example.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import numpy -from functools import reduce -import argparse -import operator - - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='Random cubical complex.', - epilog='Example: ' - './random_cubical_complex_persistence_example.py' - ' 10 10 10 - Constructs a random cubical ' - 'complex in a dimension [10, 10, 10] (aka. ' - '1000 random top dimensional cells).') -parser.add_argument('dimension', type=int, nargs="*", - help='Cubical complex dimensions') - -args = parser.parse_args() -dimension_multiplication = reduce(operator.mul, args.dimension, 1) - -if dimension_multiplication > 1: - print("#####################################################################") - print("CubicalComplex creation") - cubical_complex = gudhi.CubicalComplex(dimensions=args.dimension, - top_dimensional_cells = numpy.random.rand(dimension_multiplication)) - - print("persistence(homology_coeff_field=2, min_persistence=0)=") - print(cubical_complex.persistence(homology_coeff_field=2, min_persistence=0)) - - print("betti_numbers()=") - print(cubical_complex.betti_numbers()) diff --git a/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py b/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py deleted file mode 100755 index 0c9dfc43..00000000 --- a/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import sys -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2017 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2017 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='RipsComplex creation from ' - 'a correlation matrix read in a csv file.', - epilog='Example: ' - 'example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py ' - '-f ../data/correlation_matrix/lower_triangular_correlation_matrix.csv -e 12.0 -d 3' - '- Constructs a Rips complex with the ' - 'correlation matrix from the given csv file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-c", "--min_edge_correlation", type=float, default=0.5) -parser.add_argument("-d", "--max_dimension", type=int, default=1) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -if not (-1. < args.min_edge_correlation < 1.): - print("Wrong value of the treshold corelation (should be between -1 and 1).") - sys.exit(1) - -print("#####################################################################") -print("Caution: as persistence diagrams points will be under the diagonal,") -print("bottleneck distance and persistence graphical tool will not work") -print("properly, this is a known issue.") - -print("#####################################################################") -print("RipsComplex creation from correlation matrix read in a csv file") - -message = "RipsComplex with min_edge_correlation=" + repr(args.min_edge_correlation) -print(message) - -correlation_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=args.file) -# Given a correlation matrix M, we compute component-wise M'[i,j] = 1-M[i,j] to get a distance matrix: -distance_matrix = [[1.-correlation_matrix[i][j] for j in range(len(correlation_matrix[i]))] for i in range(len(correlation_matrix))] - -rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, - max_edge_length=1.-args.min_edge_correlation) -simplex_tree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) - -message = "Number of simplices=" + repr(simplex_tree.num_simplices()) -print(message) - -diag = simplex_tree.persistence() - -print("betti_numbers()=") -print(simplex_tree.betti_numbers()) - -# invert the persistence diagram -invert_diag = [(diag[pers][0],(1.-diag[pers][1][0], 1.-diag[pers][1][1])) for pers in range(len(diag))] - -if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(invert_diag, band=args.band) - pplot.show() diff --git a/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py b/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py deleted file mode 100755 index 4d2ed577..00000000 --- a/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py +++ /dev/null @@ -1,67 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='RipsComplex creation from ' - 'a distance matrix read in a csv file.', - epilog='Example: ' - 'example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py ' - '-f ../data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3' - '- Constructs a Rips complex with the ' - 'distance matrix from the given csv file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-e", "--max_edge_length", type=float, default=0.5) -parser.add_argument("-d", "--max_dimension", type=int, default=1) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -print("#####################################################################") -print("RipsComplex creation from distance matrix read in a csv file") - -message = "RipsComplex with max_edge_length=" + repr(args.max_edge_length) -print(message) - -distance_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=args.file) -rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, max_edge_length=args.max_edge_length) -simplex_tree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) - -message = "Number of simplices=" + repr(simplex_tree.num_simplices()) -print(message) - -diag = simplex_tree.persistence() - -print("betti_numbers()=") -print(simplex_tree.betti_numbers()) - -if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(diag, band=args.band) - pplot.show() diff --git a/cython/example/rips_complex_diagram_persistence_from_off_file_example.py b/cython/example/rips_complex_diagram_persistence_from_off_file_example.py deleted file mode 100755 index d15d5eb0..00000000 --- a/cython/example/rips_complex_diagram_persistence_from_off_file_example.py +++ /dev/null @@ -1,74 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='RipsComplex creation from ' - 'points read in a OFF file.', - epilog='Example: ' - 'example/rips_complex_diagram_persistence_from_off_file_example.py ' - '-f ../data/points/tore3D_300.off -a 0.6' - '- Constructs a Rips complex with the ' - 'points from the given OFF file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-e", "--max_edge_length", type=float, default=0.5) -parser.add_argument("-d", "--max_dimension", type=int, default=1) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -with open(args.file, 'r') as f: - first_line = f.readline() - if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): - print("#####################################################################") - print("RipsComplex creation from points read in a OFF file") - - message = "RipsComplex with max_edge_length=" + repr(args.max_edge_length) - print(message) - - point_cloud = gudhi.read_off(off_file=args.file) - rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=args.max_edge_length) - simplex_tree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) - - message = "Number of simplices=" + repr(simplex_tree.num_simplices()) - print(message) - - diag = simplex_tree.persistence() - - print("betti_numbers()=") - print(simplex_tree.betti_numbers()) - - if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(diag, band=args.band) - pplot.show() - else: - print(args.file, "is not a valid OFF file") - - f.close() diff --git a/cython/example/rips_complex_from_points_example.py b/cython/example/rips_complex_from_points_example.py deleted file mode 100755 index ffa9d91f..00000000 --- a/cython/example/rips_complex_from_points_example.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python - -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -print("#####################################################################") -print("RipsComplex creation from points") -rips = gudhi.RipsComplex(points=[[0, 0], [1, 0], [0, 1], [1, 1]], - max_edge_length=42) - -simplex_tree = rips.create_simplex_tree(max_dimension=1) - -print("filtrations=", simplex_tree.get_filtration()) -print("star([0])=", simplex_tree.get_star([0])) -print("coface([0], 1)=", simplex_tree.get_cofaces([0], 1)) diff --git a/cython/example/rips_persistence_diagram.py b/cython/example/rips_persistence_diagram.py deleted file mode 100755 index 7a6a9f46..00000000 --- a/cython/example/rips_persistence_diagram.py +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env python - -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Marc Glisse - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Marc Glisse" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -print("#####################################################################") -print("RipsComplex creation from points") -rips = gudhi.RipsComplex(points=[[0, 0], [1, 0], [0, 1], [1, 1]], - max_edge_length=42) - -simplex_tree = rips.create_simplex_tree(max_dimension=1) - - -diag = simplex_tree.persistence(homology_coeff_field=2, min_persistence=0) -print("diag=", diag) - -pplot = gudhi.plot_persistence_diagram(diag) -pplot.show() diff --git a/cython/example/simplex_tree_example.py b/cython/example/simplex_tree_example.py deleted file mode 100755 index 28679015..00000000 --- a/cython/example/simplex_tree_example.py +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env python - -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -print("#####################################################################") -print("SimplexTree creation from insertion") - -st = gudhi.SimplexTree() - -if st.insert([0, 1]): - print("Inserted !!") -else: - print("Not inserted...") - -if st.find([0, 1]): - print("Found !!") -else: - print("Not found...") - -if st.insert([0, 1, 2], filtration=4.0): - print("Inserted !!") -else: - print("Not inserted...") - -print("dimension=", st.dimension()) - -st.initialize_filtration() -print("filtration=", st.get_filtration()) -print("filtration[1, 2]=", st.filtration([1, 2])) -print("filtration[4, 2]=", st.filtration([4, 2])) - -print("num_simplices=", st.num_simplices()) -print("num_vertices=", st.num_vertices()) - -print("skeleton[2]=", st.get_skeleton(2)) -print("skeleton[1]=", st.get_skeleton(1)) -print("skeleton[0]=", st.get_skeleton(0)) diff --git a/cython/example/tangential_complex_plain_homology_from_off_file_example.py b/cython/example/tangential_complex_plain_homology_from_off_file_example.py deleted file mode 100755 index 0f8f5e80..00000000 --- a/cython/example/tangential_complex_plain_homology_from_off_file_example.py +++ /dev/null @@ -1,69 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='TangentialComplex creation from ' - 'points read in a OFF file.', - epilog='Example: ' - 'example/tangential_complex_plain_homology_from_off_file_example.py ' - '-f ../data/points/tore3D_300.off -i 3' - '- Constructs a tangential complex with the ' - 'points from the given OFF file') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-i", "--intrisic_dim", type=int, required=True) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -with open(args.file, 'r') as f: - first_line = f.readline() - if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): - print("#####################################################################") - print("TangentialComplex creation from points read in a OFF file") - - tc = gudhi.TangentialComplex(intrisic_dim = args.intrisic_dim, off_file=args.file) - st = tc.create_simplex_tree() - - message = "Number of simplices=" + repr(st.num_simplices()) - print(message) - - diag = st.persistence(persistence_dim_max = True) - - print("betti_numbers()=") - print(st.betti_numbers()) - - if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(diag, band=args.band) - pplot.show() - else: - print(args.file, "is not a valid OFF file") - - f.close() diff --git a/cython/example/voronoi_graph_induced_complex.py b/cython/example/voronoi_graph_induced_complex.py deleted file mode 100755 index 8266a0e4..00000000 --- a/cython/example/voronoi_graph_induced_complex.py +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2018 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2018 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='Voronoi GIC ' - 'from points read in a OFF file.', - epilog='Example: ' - 'example/voronoi_graph_induced_complex.py ' - '-f ../data/points/human.off -n 700 -v' - '- Constructs the Voronoi GIC with the ' - 'points from the given OFF file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-n", "--subsample-nb-points", type=int, default=100) -parser.add_argument("-v", "--verbose", default=False, action='store_true' , help='Flag for program verbosity') - -args = parser.parse_args() - -nerve_complex = gudhi.CoverComplex() -nerve_complex.set_verbose(args.verbose) - -if (nerve_complex.read_point_cloud(args.file)): - nerve_complex.set_type('GIC') - nerve_complex.set_color_from_coordinate() - nerve_complex.set_graph_from_OFF() - nerve_complex.set_cover_from_Voronoi(args.subsample_nb_points) - nerve_complex.find_simplices() - nerve_complex.plot_off() - simplex_tree = nerve_complex.create_simplex_tree() - nerve_complex.compute_PD() - if (args.verbose): - print('Iterator on graph induced complex simplices') - result_str = 'Graph induced complex is of dimension ' + \ - repr(simplex_tree.dimension()) + ' - ' + \ - repr(simplex_tree.num_simplices()) + ' simplices - ' + \ - repr(simplex_tree.num_vertices()) + ' vertices.' - print(result_str) - for filtered_value in simplex_tree.get_filtration(): - print(filtered_value[0]) diff --git a/cython/example/witness_complex_from_nearest_landmark_table.py b/cython/example/witness_complex_from_nearest_landmark_table.py deleted file mode 100755 index e6b295ee..00000000 --- a/cython/example/witness_complex_from_nearest_landmark_table.py +++ /dev/null @@ -1,46 +0,0 @@ -#!/usr/bin/env python - -from gudhi import StrongWitnessComplex, SimplexTree - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -print("#####################################################################") -print("WitnessComplex creation from nearest landmark table") -nearest_landmark_table = [[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]], - [[1, 0], [2, 1], [3, 2], [4, 3], [0, 4]], - [[2, 0], [3, 1], [4, 2], [0, 3], [1, 4]], - [[3, 0], [4, 1], [0, 2], [1, 3], [2, 4]], - [[4, 0], [0, 1], [1, 2], [2, 3], [3, 4]]] - -witness_complex = StrongWitnessComplex(nearest_landmark_table=nearest_landmark_table) -simplex_tree = witness_complex.create_simplex_tree(max_alpha_square=4.1) - -message = "Number of simplices: " + repr(simplex_tree.num_simplices()) -print(message) - -diag = simplex_tree.persistence(min_persistence=-0.1, homology_coeff_field=11) -print(diag) diff --git a/cython/gudhi.pyx.in b/cython/gudhi.pyx.in deleted file mode 100644 index 0d4b966b..00000000 --- a/cython/gudhi.pyx.in +++ /dev/null @@ -1,45 +0,0 @@ -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" -__version__ = "@GUDHI_VERSION@" -# This variable is used by doctest to find files -__root_source_dir__ = "@CMAKE_SOURCE_DIR@" -__debug_info__ = @GUDHI_CYTHON_DEBUG_INFO@ - -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/off_reader.pyx' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/simplex_tree.pyx' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/rips_complex.pyx' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/cubical_complex.pyx' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/periodic_cubical_complex.pyx' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/persistence_graphical_tools.py' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/reader_utils.pyx' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/witness_complex.pyx' -include '@CMAKE_CURRENT_SOURCE_DIR@/cython/strong_witness_complex.pyx' -@GUDHI_CYTHON_ALPHA_COMPLEX@ -@GUDHI_CYTHON_EUCLIDEAN_WITNESS_COMPLEX@ -@GUDHI_CYTHON_SUBSAMPLING@ -@GUDHI_CYTHON_TANGENTIAL_COMPLEX@ -@GUDHI_CYTHON_BOTTLENECK_DISTANCE@ -@GUDHI_CYTHON_NERVE_GIC@ diff --git a/cython/include/Alpha_complex_interface.h b/cython/include/Alpha_complex_interface.h deleted file mode 100644 index 8cf527fc..00000000 --- a/cython/include/Alpha_complex_interface.h +++ /dev/null @@ -1,82 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_ALPHA_COMPLEX_INTERFACE_H_ -#define INCLUDE_ALPHA_COMPLEX_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/Alpha_complex.h> -#include <CGAL/Epick_d.h> - -#include "Simplex_tree_interface.h" - -#include <iostream> -#include <vector> -#include <string> - -namespace Gudhi { - -namespace alpha_complex { - -class Alpha_complex_interface { - using Dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; - using Point_d = Dynamic_kernel::Point_d; - - public: - Alpha_complex_interface(const std::vector<std::vector<double>>& points) { - alpha_complex_ = new Alpha_complex<Dynamic_kernel>(points); - } - - Alpha_complex_interface(const std::string& off_file_name, bool from_file = true) { - alpha_complex_ = new Alpha_complex<Dynamic_kernel>(off_file_name); - } - - ~Alpha_complex_interface() { - delete alpha_complex_; - } - - std::vector<double> get_point(int vh) { - std::vector<double> vd; - try { - Point_d ph = alpha_complex_->get_point(vh); - for (auto coord = ph.cartesian_begin(); coord < ph.cartesian_end(); coord++) - vd.push_back(*coord); - } catch (std::out_of_range outofrange) { - // std::out_of_range is thrown in case not found. Other exceptions must be re-thrown - } - return vd; - } - - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square) { - alpha_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); - } - - private: - Alpha_complex<Dynamic_kernel>* alpha_complex_; -}; - -} // namespace alpha_complex - -} // namespace Gudhi - -#endif // INCLUDE_ALPHA_COMPLEX_INTERFACE_H_ diff --git a/cython/include/Bottleneck_distance_interface.h b/cython/include/Bottleneck_distance_interface.h deleted file mode 100644 index 5ad9d77d..00000000 --- a/cython/include/Bottleneck_distance_interface.h +++ /dev/null @@ -1,53 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_BOTTLENECK_DISTANCE_INTERFACE_H_ -#define INCLUDE_BOTTLENECK_DISTANCE_INTERFACE_H_ - -#include <gudhi/Bottleneck.h> - -#include <iostream> -#include <vector> -#include <utility> // for std::pair - -namespace Gudhi { - -namespace persistence_diagram { - - // bottleneck_distance function renamed for the python function can be called bottleneck_dstance - double bottleneck(const std::vector<std::pair<double, double>>& diag1, - const std::vector<std::pair<double, double>>& diag2, - double e) { - return bottleneck_distance(diag1, diag2, e); - } - - double bottleneck(const std::vector<std::pair<double, double>>& diag1, - const std::vector<std::pair<double, double>>& diag2) { - return bottleneck_distance(diag1, diag2); - } - -} // namespace persistence_diagram - -} // namespace Gudhi - - -#endif // INCLUDE_BOTTLENECK_DISTANCE_INTERFACE_H_ diff --git a/cython/include/Cubical_complex_interface.h b/cython/include/Cubical_complex_interface.h deleted file mode 100644 index 85b717b3..00000000 --- a/cython/include/Cubical_complex_interface.h +++ /dev/null @@ -1,62 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_CUBICAL_COMPLEX_INTERFACE_H_ -#define INCLUDE_CUBICAL_COMPLEX_INTERFACE_H_ - -#include <gudhi/Bitmap_cubical_complex.h> -#include <gudhi/Bitmap_cubical_complex_base.h> -#include <gudhi/Bitmap_cubical_complex_periodic_boundary_conditions_base.h> - -#include <iostream> -#include <vector> -#include <string> - -namespace Gudhi { - -namespace cubical_complex { - -template<typename CubicalComplexOptions = Bitmap_cubical_complex_base<double>> -class Cubical_complex_interface : public Bitmap_cubical_complex<CubicalComplexOptions> { - public: - Cubical_complex_interface(const std::vector<unsigned>& dimensions, - const std::vector<double>& top_dimensional_cells) - : Bitmap_cubical_complex<CubicalComplexOptions>(dimensions, top_dimensional_cells) { - } - - Cubical_complex_interface(const std::vector<unsigned>& dimensions, - const std::vector<double>& top_dimensional_cells, - const std::vector<bool>& periodic_dimensions) - : Bitmap_cubical_complex<CubicalComplexOptions>(dimensions, top_dimensional_cells, periodic_dimensions) { - } - - Cubical_complex_interface(const std::string& perseus_file) - : Bitmap_cubical_complex<CubicalComplexOptions>(perseus_file.c_str()) { - } -}; - -} // namespace cubical_complex - -} // namespace Gudhi - -#endif // INCLUDE_CUBICAL_COMPLEX_INTERFACE_H_ - diff --git a/cython/include/Euclidean_strong_witness_complex_interface.h b/cython/include/Euclidean_strong_witness_complex_interface.h deleted file mode 100644 index d86355d6..00000000 --- a/cython/include/Euclidean_strong_witness_complex_interface.h +++ /dev/null @@ -1,93 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_EUCLIDEAN_STRONG_WITNESS_COMPLEX_INTERFACE_H_ -#define INCLUDE_EUCLIDEAN_STRONG_WITNESS_COMPLEX_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/Euclidean_strong_witness_complex.h> - -#include "Simplex_tree_interface.h" - -#include <CGAL/Epick_d.h> - -#include <vector> -#include <utility> // std::pair -#include <iostream> -#include <cstddef> - -namespace Gudhi { - -namespace witness_complex { - - -class Euclidean_strong_witness_complex_interface { - using Dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; - using Point_d = Dynamic_kernel::Point_d; - - typedef typename Simplex_tree<>::Simplex_key Simplex_key; - - public: - Euclidean_strong_witness_complex_interface(const std::vector<std::vector<double>>& landmarks, - const std::vector<std::vector<double>>& witnesses) { - landmarks_.reserve(landmarks.size()); - for (auto& landmark : landmarks) - landmarks_.emplace_back(landmark.begin(), landmark.end()); - witness_complex_ = new Euclidean_strong_witness_complex<Dynamic_kernel>(landmarks_, witnesses); - } - - ~Euclidean_strong_witness_complex_interface() { - delete witness_complex_; - } - - void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square, - std::size_t limit_dimension) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); - } - - void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); - } - - std::vector<double> get_point(unsigned vh) { - std::vector<double> vd; - if (vh < landmarks_.size()) { - Point_d ph = witness_complex_->get_point(vh); - for (auto coord = ph.cartesian_begin(); coord < ph.cartesian_end(); coord++) - vd.push_back(*coord); - } - return vd; - } - - private: - std::vector<Point_d> landmarks_; - Euclidean_strong_witness_complex<Dynamic_kernel>* witness_complex_; -}; - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // INCLUDE_EUCLIDEAN_STRONG_WITNESS_COMPLEX_INTERFACE_H_ - diff --git a/cython/include/Euclidean_witness_complex_interface.h b/cython/include/Euclidean_witness_complex_interface.h deleted file mode 100644 index dc303533..00000000 --- a/cython/include/Euclidean_witness_complex_interface.h +++ /dev/null @@ -1,92 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_EUCLIDEAN_WITNESS_COMPLEX_INTERFACE_H_ -#define INCLUDE_EUCLIDEAN_WITNESS_COMPLEX_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/Euclidean_witness_complex.h> - -#include "Simplex_tree_interface.h" - -#include <CGAL/Epick_d.h> - -#include <vector> -#include <utility> // std::pair -#include <iostream> -#include <cstddef> - -namespace Gudhi { - -namespace witness_complex { - - -class Euclidean_witness_complex_interface { - using Dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; - using Point_d = Dynamic_kernel::Point_d; - - typedef typename Simplex_tree<>::Simplex_key Simplex_key; - - public: - Euclidean_witness_complex_interface(const std::vector<std::vector<double>>& landmarks, - const std::vector<std::vector<double>>& witnesses) { - landmarks_.reserve(landmarks.size()); - for (auto& landmark : landmarks) - landmarks_.emplace_back(landmark.begin(), landmark.end()); - witness_complex_ = new Euclidean_witness_complex<Dynamic_kernel>(landmarks_, witnesses); - } - - ~Euclidean_witness_complex_interface() { - delete witness_complex_; - } - - void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square, std::size_t limit_dimension) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); - } - - void create_simplex_tree(Gudhi::Simplex_tree<>* simplex_tree, double max_alpha_square) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); - } - - std::vector<double> get_point(unsigned vh) { - std::vector<double> vd; - if (vh < landmarks_.size()) { - Point_d ph = witness_complex_->get_point(vh); - for (auto coord = ph.cartesian_begin(); coord < ph.cartesian_end(); coord++) - vd.push_back(*coord); - } - return vd; - } - - private: - std::vector<Point_d> landmarks_; - Euclidean_witness_complex<Dynamic_kernel>* witness_complex_; -}; - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // INCLUDE_EUCLIDEAN_WITNESS_COMPLEX_INTERFACE_H_ - diff --git a/cython/include/Nerve_gic_interface.h b/cython/include/Nerve_gic_interface.h deleted file mode 100644 index aa71e2a6..00000000 --- a/cython/include/Nerve_gic_interface.h +++ /dev/null @@ -1,61 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2018 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_NERVE_GIC_INTERFACE_H_ -#define INCLUDE_NERVE_GIC_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/distance_functions.h> -#include <gudhi/GIC.h> - -#include "Simplex_tree_interface.h" - -#include <iostream> -#include <vector> -#include <string> - -namespace Gudhi { - -namespace cover_complex { - -class Nerve_gic_interface : public Cover_complex<std::vector<double>> { - public: - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree) { - create_complex(*simplex_tree); - simplex_tree->initialize_filtration(); - } - void set_cover_from_Euclidean_Voronoi(int m) { - set_cover_from_Voronoi(Gudhi::Euclidean_distance(), m); - } - double set_graph_from_automatic_euclidean_rips(int N) { - return set_graph_from_automatic_rips(Gudhi::Euclidean_distance(), N); - } - void set_graph_from_euclidean_rips(double threshold) { - set_graph_from_rips(threshold, Gudhi::Euclidean_distance()); - } -}; - -} // namespace cover_complex - -} // namespace Gudhi - -#endif // INCLUDE_NERVE_GIC_INTERFACE_H_ diff --git a/cython/include/Off_reader_interface.h b/cython/include/Off_reader_interface.h deleted file mode 100644 index f6b14f38..00000000 --- a/cython/include/Off_reader_interface.h +++ /dev/null @@ -1,42 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_OFF_READER_INTERFACE_H_ -#define INCLUDE_OFF_READER_INTERFACE_H_ - -#include <gudhi/Points_off_io.h> - -#include <iostream> -#include <vector> -#include <string> - -namespace Gudhi { - -std::vector<std::vector<double>> read_points_from_OFF_file(const std::string& off_file) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - return off_reader.get_point_cloud(); -} - -} // namespace Gudhi - -#endif // INCLUDE_OFF_READER_INTERFACE_H_ - diff --git a/cython/include/Persistent_cohomology_interface.h b/cython/include/Persistent_cohomology_interface.h deleted file mode 100644 index 8cf71a4e..00000000 --- a/cython/include/Persistent_cohomology_interface.h +++ /dev/null @@ -1,121 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_PERSISTENT_COHOMOLOGY_INTERFACE_H_ -#define INCLUDE_PERSISTENT_COHOMOLOGY_INTERFACE_H_ - -#include <gudhi/Persistent_cohomology.h> - -#include <vector> -#include <utility> // for std::pair -#include <algorithm> // for sort - -namespace Gudhi { - -template<class FilteredComplex> -class Persistent_cohomology_interface : public -persistent_cohomology::Persistent_cohomology<FilteredComplex, persistent_cohomology::Field_Zp> { - private: - /* - * Compare two intervals by dimension, then by length. - */ - struct cmp_intervals_by_dim_then_length { - explicit cmp_intervals_by_dim_then_length(FilteredComplex * sc) - : sc_(sc) { } - - template<typename Persistent_interval> - bool operator()(const Persistent_interval & p1, const Persistent_interval & p2) { - if (sc_->dimension(get < 0 > (p1)) == sc_->dimension(get < 0 > (p2))) - return (sc_->filtration(get < 1 > (p1)) - sc_->filtration(get < 0 > (p1)) - > sc_->filtration(get < 1 > (p2)) - sc_->filtration(get < 0 > (p2))); - else - return (sc_->dimension(get < 0 > (p1)) > sc_->dimension(get < 0 > (p2))); - } - FilteredComplex* sc_; - }; - - public: - Persistent_cohomology_interface(FilteredComplex* stptr) - : persistent_cohomology::Persistent_cohomology<FilteredComplex, persistent_cohomology::Field_Zp>(*stptr), - stptr_(stptr) { } - - Persistent_cohomology_interface(FilteredComplex* stptr, bool persistence_dim_max) - : persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>(*stptr, persistence_dim_max), - stptr_(stptr) { } - - std::vector<std::pair<int, std::pair<double, double>>> get_persistence(int homology_coeff_field, - double min_persistence) { - persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>::init_coefficients(homology_coeff_field); - persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>::compute_persistent_cohomology(min_persistence); - - // Custom sort and output persistence - cmp_intervals_by_dim_then_length cmp(stptr_); - auto persistent_pairs = persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>::get_persistent_pairs(); - std::sort(std::begin(persistent_pairs), std::end(persistent_pairs), cmp); - - std::vector<std::pair<int, std::pair<double, double>>> persistence; - for (auto pair : persistent_pairs) { - persistence.push_back(std::make_pair(stptr_->dimension(get<0>(pair)), - std::make_pair(stptr_->filtration(get<0>(pair)), - stptr_->filtration(get<1>(pair))))); - } - return persistence; - } - - std::vector<std::pair<std::vector<int>, std::vector<int>>> persistence_pairs() { - auto pairs = persistent_cohomology::Persistent_cohomology<FilteredComplex, - persistent_cohomology::Field_Zp>::get_persistent_pairs(); - - std::vector<std::pair<std::vector<int>, std::vector<int>>> persistence_pairs; - persistence_pairs.reserve(pairs.size()); - for (auto pair : pairs) { - std::vector<int> birth; - if (get<0>(pair) != stptr_->null_simplex()) { - for (auto vertex : stptr_->simplex_vertex_range(get<0>(pair))) { - birth.push_back(vertex); - } - } - - std::vector<int> death; - if (get<1>(pair) != stptr_->null_simplex()) { - for (auto vertex : stptr_->simplex_vertex_range(get<1>(pair))) { - death.push_back(vertex); - } - } - - persistence_pairs.push_back(std::make_pair(birth, death)); - } - return persistence_pairs; - } - - private: - // A copy - FilteredComplex* stptr_; -}; - -} // namespace Gudhi - -#endif // INCLUDE_PERSISTENT_COHOMOLOGY_INTERFACE_H_ diff --git a/cython/include/Reader_utils_interface.h b/cython/include/Reader_utils_interface.h deleted file mode 100644 index 5f7527d9..00000000 --- a/cython/include/Reader_utils_interface.h +++ /dev/null @@ -1,56 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2017 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_READER_UTILS_INTERFACE_H_ -#define INCLUDE_READER_UTILS_INTERFACE_H_ - -#include <gudhi/reader_utils.h> - -#include <iostream> -#include <vector> -#include <string> -#include <map> -#include <utility> // for pair<> - -namespace Gudhi { - -// Redefine functions with a different name in order the original name can be used in the Python version. -std::vector<std::vector<double>> read_matrix_from_csv_file(const std::string& filename, - const char separator = ';') { - return read_lower_triangular_matrix_from_csv_file<double>(filename, separator); -} - -inline std::map<int, std::vector<std::pair<double, double>>> - read_pers_intervals_grouped_by_dimension(std::string const& filename) { - return read_persistence_intervals_grouped_by_dimension(filename); -} - -inline std::vector<std::pair<double, double>> - read_pers_intervals_in_dimension(std::string const& filename, int only_this_dim = -1) { - return read_persistence_intervals_in_dimension(filename, only_this_dim); -} - - -} // namespace Gudhi - - -#endif // INCLUDE_READER_UTILS_INTERFACE_H_ diff --git a/cython/include/Rips_complex_interface.h b/cython/include/Rips_complex_interface.h deleted file mode 100644 index 8b6c9c35..00000000 --- a/cython/include/Rips_complex_interface.h +++ /dev/null @@ -1,74 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_RIPS_COMPLEX_INTERFACE_H_ -#define INCLUDE_RIPS_COMPLEX_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/Rips_complex.h> -#include <gudhi/distance_functions.h> - -#include "Simplex_tree_interface.h" - -#include <iostream> -#include <vector> -#include <utility> // std::pair -#include <string> - -namespace Gudhi { - -namespace rips_complex { - -class Rips_complex_interface { - using Point_d = std::vector<double>; - using Distance_matrix = std::vector<std::vector<Simplex_tree_interface<>::Filtration_value>>; - - public: - Rips_complex_interface(const std::vector<std::vector<double>>& values, double threshold, bool euclidean) { - if (euclidean) { - // Rips construction where values is a vector of points - rips_complex_ = new Rips_complex<Simplex_tree_interface<>::Filtration_value>(values, threshold, - Gudhi::Euclidean_distance()); - } else { - // Rips construction where values is a distance matrix - rips_complex_ = new Rips_complex<Simplex_tree_interface<>::Filtration_value>(values, threshold); - } - } - - ~Rips_complex_interface() { - delete rips_complex_; - } - - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, int dim_max) { - rips_complex_->create_complex(*simplex_tree, dim_max); - simplex_tree->initialize_filtration(); - } - - private: - Rips_complex<Simplex_tree_interface<>::Filtration_value>* rips_complex_; -}; - -} // namespace rips_complex - -} // namespace Gudhi - -#endif // INCLUDE_RIPS_COMPLEX_INTERFACE_H_ diff --git a/cython/include/Simplex_tree_interface.h b/cython/include/Simplex_tree_interface.h deleted file mode 100644 index 3481eeff..00000000 --- a/cython/include/Simplex_tree_interface.h +++ /dev/null @@ -1,158 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_SIMPLEX_TREE_INTERFACE_H_ -#define INCLUDE_SIMPLEX_TREE_INTERFACE_H_ - -#include <gudhi/graph_simplicial_complex.h> -#include <gudhi/distance_functions.h> -#include <gudhi/Simplex_tree.h> -#include <gudhi/Points_off_io.h> - -#include "Persistent_cohomology_interface.h" - -#include <iostream> -#include <vector> -#include <utility> // std::pair - -namespace Gudhi { - -template<typename SimplexTreeOptions = Simplex_tree_options_full_featured> -class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> { - public: - using Base = Simplex_tree<SimplexTreeOptions>; - using Filtration_value = typename Base::Filtration_value; - using Vertex_handle = typename Base::Vertex_handle; - using Simplex_handle = typename Base::Simplex_handle; - using Insertion_result = typename std::pair<Simplex_handle, bool>; - using Simplex = std::vector<Vertex_handle>; - using Complex = std::vector<std::pair<Simplex, Filtration_value>>; - - public: - bool find_simplex(const Simplex& vh) { - return (Base::find(vh) != Base::null_simplex()); - } - - void assign_simplex_filtration(const Simplex& vh, Filtration_value filtration) { - Base::assign_filtration(Base::find(vh), filtration); - } - - bool insert(const Simplex& simplex, Filtration_value filtration = 0) { - Insertion_result result = Base::insert_simplex_and_subfaces(simplex, filtration); - return (result.second); - } - - // Do not interface this function, only used in alpha complex interface for complex creation - bool insert_simplex(const Simplex& simplex, Filtration_value filtration = 0) { - Insertion_result result = Base::insert_simplex(simplex, filtration); - return (result.second); - } - - // Do not interface this function, only used in interface for complex creation - bool insert_simplex_and_subfaces(const Simplex& simplex, Filtration_value filtration = 0) { - Insertion_result result = Base::insert_simplex_and_subfaces(simplex, filtration); - return (result.second); - } - - // Do not interface this function, only used in strong witness interface for complex creation - bool insert_simplex(const std::vector<std::size_t>& simplex, Filtration_value filtration = 0) { - Insertion_result result = Base::insert_simplex(simplex, filtration); - return (result.second); - } - - // Do not interface this function, only used in strong witness interface for complex creation - bool insert_simplex_and_subfaces(const std::vector<std::size_t>& simplex, Filtration_value filtration = 0) { - Insertion_result result = Base::insert_simplex_and_subfaces(simplex, filtration); - return (result.second); - } - - Filtration_value simplex_filtration(const Simplex& simplex) { - return Base::filtration(Base::find(simplex)); - } - - void remove_maximal_simplex(const Simplex& simplex) { - Base::remove_maximal_simplex(Base::find(simplex)); - Base::initialize_filtration(); - } - - Complex get_filtration() { - Base::initialize_filtration(); - Complex filtrations; - for (auto f_simplex : Base::filtration_simplex_range()) { - Simplex simplex; - for (auto vertex : Base::simplex_vertex_range(f_simplex)) { - simplex.insert(simplex.begin(), vertex); - } - filtrations.push_back(std::make_pair(simplex, Base::filtration(f_simplex))); - } - return filtrations; - } - - Complex get_skeleton(int dimension) { - Complex skeletons; - for (auto f_simplex : Base::skeleton_simplex_range(dimension)) { - Simplex simplex; - for (auto vertex : Base::simplex_vertex_range(f_simplex)) { - simplex.insert(simplex.begin(), vertex); - } - skeletons.push_back(std::make_pair(simplex, Base::filtration(f_simplex))); - } - return skeletons; - } - - Complex get_star(const Simplex& simplex) { - Complex star; - for (auto f_simplex : Base::star_simplex_range(Base::find(simplex))) { - Simplex simplex_star; - for (auto vertex : Base::simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; - simplex_star.insert(simplex_star.begin(), vertex); - } - std::cout << std::endl; - star.push_back(std::make_pair(simplex_star, Base::filtration(f_simplex))); - } - return star; - } - - Complex get_cofaces(const Simplex& simplex, int dimension) { - Complex cofaces; - for (auto f_simplex : Base::cofaces_simplex_range(Base::find(simplex), dimension)) { - Simplex simplex_coface; - for (auto vertex : Base::simplex_vertex_range(f_simplex)) { - std::cout << vertex << " "; - simplex_coface.insert(simplex_coface.begin(), vertex); - } - std::cout << std::endl; - cofaces.push_back(std::make_pair(simplex_coface, Base::filtration(f_simplex))); - } - return cofaces; - } - - void create_persistence(Gudhi::Persistent_cohomology_interface<Base>* pcoh) { - Base::initialize_filtration(); - pcoh = new Gudhi::Persistent_cohomology_interface<Base>(*this); - } -}; - -} // namespace Gudhi - -#endif // INCLUDE_SIMPLEX_TREE_INTERFACE_H_ diff --git a/cython/include/Strong_witness_complex_interface.h b/cython/include/Strong_witness_complex_interface.h deleted file mode 100644 index 3c72c916..00000000 --- a/cython/include/Strong_witness_complex_interface.h +++ /dev/null @@ -1,73 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_STRONG_WITNESS_COMPLEX_INTERFACE_H_ -#define INCLUDE_STRONG_WITNESS_COMPLEX_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/Strong_witness_complex.h> - -#include "Simplex_tree_interface.h" - -#include <vector> -#include <utility> // std::pair -#include <iostream> -#include <cstddef> - -namespace Gudhi { - -namespace witness_complex { - -class Strong_witness_complex_interface { - using Nearest_landmark_range = std::vector<std::pair<std::size_t, double>>; - using Nearest_landmark_table = std::vector<Nearest_landmark_range>; - - public: - Strong_witness_complex_interface(const Nearest_landmark_table& nlt) { - witness_complex_ = new Strong_witness_complex<Nearest_landmark_table>(nlt); - } - - ~Strong_witness_complex_interface() { - delete witness_complex_; - } - - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square, - std::size_t limit_dimension) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); - } - - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, - double max_alpha_square) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); - } - - private: - Strong_witness_complex<Nearest_landmark_table>* witness_complex_; -}; - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // INCLUDE_STRONG_WITNESS_COMPLEX_INTERFACE_H_ diff --git a/cython/include/Subsampling_interface.h b/cython/include/Subsampling_interface.h deleted file mode 100644 index f990da0c..00000000 --- a/cython/include/Subsampling_interface.h +++ /dev/null @@ -1,119 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_SUBSAMPLING_INTERFACE_H_ -#define INCLUDE_SUBSAMPLING_INTERFACE_H_ - -#include <gudhi/choose_n_farthest_points.h> -#include <gudhi/pick_n_random_points.h> -#include <gudhi/sparsify_point_set.h> -#include <gudhi/Points_off_io.h> -#include <CGAL/Epick_d.h> - -#include <iostream> -#include <vector> -#include <string> - -namespace Gudhi { - -namespace subsampling { - -using Subsampling_dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; -using Subsampling_point_d = Subsampling_dynamic_kernel::Point_d; -using Subsampling_ft = Subsampling_dynamic_kernel::FT; - -// ------ choose_n_farthest_points ------ -std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, - unsigned nb_points) { - std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, random_starting_point, std::back_inserter(landmarks)); - - return landmarks; -} - -std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, - unsigned nb_points, unsigned starting_point) { - std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, starting_point, std::back_inserter(landmarks)); - - return landmarks; -} - -std::vector<std::vector<double>> subsampling_n_farthest_points_from_file(const std::string& off_file, - unsigned nb_points) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_n_farthest_points(points, nb_points); -} - -std::vector<std::vector<double>> subsampling_n_farthest_points_from_file(const std::string& off_file, - unsigned nb_points, unsigned starting_point) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_n_farthest_points(points, nb_points, starting_point); -} - -// ------ pick_n_random_points ------ -std::vector<std::vector<double>> subsampling_n_random_points(const std::vector<std::vector<double>>& points, - unsigned nb_points) { - std::vector<std::vector<double>> landmarks; - pick_n_random_points(points, nb_points, std::back_inserter(landmarks)); - - return landmarks; -} - -std::vector<std::vector<double>> subsampling_n_random_points_from_file(const std::string& off_file, - unsigned nb_points) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_n_random_points(points, nb_points); -} - -// ------ sparsify_point_set ------ -std::vector<std::vector<double>> subsampling_sparsify_points(const std::vector<std::vector<double>>& points, - double min_squared_dist) { - std::vector<Subsampling_point_d> input, output; - for (auto point : points) - input.push_back(Subsampling_point_d(point.size(), point.begin(), point.end())); - Subsampling_dynamic_kernel k; - sparsify_point_set(k, input, min_squared_dist, std::back_inserter(output)); - - std::vector<std::vector<double>> landmarks; - for (auto point : output) - landmarks.push_back(std::vector<double>(point.cartesian_begin(), point.cartesian_end())); - return landmarks; -} - -std::vector<std::vector<double>> subsampling_sparsify_points_from_file(const std::string& off_file, - double min_squared_dist) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_sparsify_points(points, min_squared_dist); -} - -} // namespace subsampling - -} // namespace Gudhi - -#endif // INCLUDE_SUBSAMPLING_INTERFACE_H_ diff --git a/cython/include/Tangential_complex_interface.h b/cython/include/Tangential_complex_interface.h deleted file mode 100644 index 71418886..00000000 --- a/cython/include/Tangential_complex_interface.h +++ /dev/null @@ -1,116 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_TANGENTIAL_COMPLEX_INTERFACE_H_ -#define INCLUDE_TANGENTIAL_COMPLEX_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/Tangential_complex.h> -#include <gudhi/Points_off_io.h> -#include <CGAL/Epick_d.h> - -#include "Simplex_tree_interface.h" - -#include <vector> -#include <utility> // std::pair -#include <iostream> -#include <string> - -namespace Gudhi { - -namespace tangential_complex { - -class Tangential_complex_interface { - using Dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; - using Point_d = Dynamic_kernel::Point_d; - using TC = Tangential_complex<Dynamic_kernel, CGAL::Dynamic_dimension_tag, CGAL::Parallel_tag>; - - public: - Tangential_complex_interface(int intrisic_dim, const std::vector<std::vector<double>>& points) { - Dynamic_kernel k; - - tangential_complex_ = new TC(points, intrisic_dim, k); - tangential_complex_->compute_tangential_complex(); - num_inconsistencies_ = tangential_complex_->number_of_inconsistent_simplices(); - } - - Tangential_complex_interface(int intrisic_dim, const std::string& off_file_name, bool from_file = true) { - Dynamic_kernel k; - - Gudhi::Points_off_reader<Point_d> off_reader(off_file_name); - std::vector<Point_d> points = off_reader.get_point_cloud(); - - tangential_complex_ = new TC(points, intrisic_dim, k); - tangential_complex_->compute_tangential_complex(); - num_inconsistencies_ = tangential_complex_->number_of_inconsistent_simplices(); - } - - ~Tangential_complex_interface() { - delete tangential_complex_; - } - - std::vector<double> get_point(unsigned vh) { - std::vector<double> vd; - if (vh < tangential_complex_->number_of_vertices()) { - Point_d ph = tangential_complex_->get_point(vh); - for (auto coord = ph.cartesian_begin(); coord < ph.cartesian_end(); coord++) - vd.push_back(*coord); - } - return vd; - } - - unsigned number_of_vertices() { - return tangential_complex_->number_of_vertices(); - } - - unsigned number_of_simplices() { - return num_inconsistencies_.num_simplices; - } - - unsigned number_of_inconsistent_simplices() { - return num_inconsistencies_.num_inconsistent_simplices; - } - - unsigned number_of_inconsistent_stars() { - return num_inconsistencies_.num_inconsistent_stars; - } - - void fix_inconsistencies_using_perturbation(double max_perturb, double time_limit) { - tangential_complex_->fix_inconsistencies_using_perturbation(max_perturb, time_limit); - num_inconsistencies_ = tangential_complex_->number_of_inconsistent_simplices(); - } - - void create_simplex_tree(Simplex_tree<>* simplex_tree) { - tangential_complex_->create_complex<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>(*simplex_tree); - simplex_tree->initialize_filtration(); - } - - private: - TC* tangential_complex_; - TC::Num_inconsistencies num_inconsistencies_; -}; - -} // namespace tangential_complex - -} // namespace Gudhi - -#endif // INCLUDE_TANGENTIAL_COMPLEX_INTERFACE_H_ diff --git a/cython/include/Witness_complex_interface.h b/cython/include/Witness_complex_interface.h deleted file mode 100644 index 01b372e7..00000000 --- a/cython/include/Witness_complex_interface.h +++ /dev/null @@ -1,74 +0,0 @@ -/* This file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Vincent Rouvreau - * - * Copyright (C) 2016 Inria - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef INCLUDE_WITNESS_COMPLEX_INTERFACE_H_ -#define INCLUDE_WITNESS_COMPLEX_INTERFACE_H_ - -#include <gudhi/Simplex_tree.h> -#include <gudhi/Witness_complex.h> - -#include "Simplex_tree_interface.h" - -#include <vector> -#include <utility> // std::pair -#include <iostream> -#include <cstddef> - -namespace Gudhi { - -namespace witness_complex { - -class Witness_complex_interface { - using Nearest_landmark_range = std::vector<std::pair<std::size_t, double>>; - using Nearest_landmark_table = std::vector<Nearest_landmark_range>; - - public: - Witness_complex_interface(const Nearest_landmark_table& nlt) { - witness_complex_ = new Witness_complex<Nearest_landmark_table>(nlt); - } - - ~Witness_complex_interface() { - delete witness_complex_; - } - - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square, - std::size_t limit_dimension) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square, limit_dimension); - simplex_tree->initialize_filtration(); - } - - void create_simplex_tree(Simplex_tree_interface<>* simplex_tree, - double max_alpha_square) { - witness_complex_->create_complex(*simplex_tree, max_alpha_square); - simplex_tree->initialize_filtration(); - } - - private: - Witness_complex<Nearest_landmark_table>* witness_complex_; -}; - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // INCLUDE_WITNESS_COMPLEX_INTERFACE_H_ - diff --git a/cython/setup.py.in b/cython/setup.py.in deleted file mode 100644 index 4037aab6..00000000 --- a/cython/setup.py.in +++ /dev/null @@ -1,50 +0,0 @@ -from distutils.core import setup, Extension -from Cython.Build import cythonize - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "GUDHI Editorial Board" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -gudhi = Extension( - "gudhi", - sources = ['@CMAKE_CURRENT_BINARY_DIR@/gudhi.pyx',], - language = 'c++', - extra_compile_args=[@GUDHI_CYTHON_EXTRA_COMPILE_ARGS@], - extra_link_args=[@GUDHI_CYTHON_EXTRA_LINK_ARGS@], - libraries=[@GUDHI_CYTHON_LIBRARIES@], - library_dirs=[@GUDHI_CYTHON_LIBRARY_DIRS@], - include_dirs = [@GUDHI_CYTHON_INCLUDE_DIRS@], - runtime_library_dirs=[@GUDHI_CYTHON_RUNTIME_LIBRARY_DIRS@], -) - -setup( - name = 'gudhi', - author='GUDHI Editorial Board', - author_email='gudhi-contact@lists.gforge.inria.fr', - version='@GUDHI_VERSION@', - url='http://gudhi.gforge.inria.fr/', - ext_modules = cythonize(gudhi), - install_requires = ["cython",], -) diff --git a/cython/test/test_alpha_complex.py b/cython/test/test_alpha_complex.py deleted file mode 100755 index e97f2530..00000000 --- a/cython/test/test_alpha_complex.py +++ /dev/null @@ -1,86 +0,0 @@ -from gudhi import AlphaComplex, SimplexTree - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_empty_alpha(): - alpha_complex = AlphaComplex(points=[[0,0]]) - assert alpha_complex.__is_defined() == True - -def test_infinite_alpha(): - point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - alpha_complex = AlphaComplex(points=point_list) - assert alpha_complex.__is_defined() == True - - simplex_tree = alpha_complex.create_simplex_tree() - assert simplex_tree.__is_persistence_defined() == False - - assert simplex_tree.num_simplices() == 11 - assert simplex_tree.num_vertices() == 4 - - assert simplex_tree.get_filtration() == \ - [([0], 0.0), ([1], 0.0), ([2], 0.0), ([3], 0.0), - ([0, 1], 0.25), ([0, 2], 0.25), ([1, 3], 0.25), - ([2, 3], 0.25), ([1, 2], 0.5), ([0, 1, 2], 0.5), - ([1, 2, 3], 0.5)] - assert simplex_tree.get_star([0]) == \ - [([0], 0.0), ([0, 1], 0.25), ([0, 1, 2], 0.5), - ([0, 2], 0.25)] - assert simplex_tree.get_cofaces([0], 1) == \ - [([0, 1], 0.25), ([0, 2], 0.25)] - - assert point_list[0] == alpha_complex.get_point(0) - assert point_list[1] == alpha_complex.get_point(1) - assert point_list[2] == alpha_complex.get_point(2) - assert point_list[3] == alpha_complex.get_point(3) - assert alpha_complex.get_point(4) == [] - assert alpha_complex.get_point(125) == [] - -def test_filtered_alpha(): - point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - filtered_alpha = AlphaComplex(points=point_list) - - simplex_tree = filtered_alpha.create_simplex_tree(max_alpha_square=0.25) - - assert simplex_tree.num_simplices() == 8 - assert simplex_tree.num_vertices() == 4 - - assert point_list[0] == filtered_alpha.get_point(0) - assert point_list[1] == filtered_alpha.get_point(1) - assert point_list[2] == filtered_alpha.get_point(2) - assert point_list[3] == filtered_alpha.get_point(3) - assert filtered_alpha.get_point(4) == [] - assert filtered_alpha.get_point(125) == [] - - assert simplex_tree.get_filtration() == \ - [([0], 0.0), ([1], 0.0), ([2], 0.0), ([3], 0.0), - ([0, 1], 0.25), ([0, 2], 0.25), ([1, 3], 0.25), - ([2, 3], 0.25)] - assert simplex_tree.get_star([0]) == \ - [([0], 0.0), ([0, 1], 0.25), ([0, 2], 0.25)] - assert simplex_tree.get_cofaces([0], 1) == \ - [([0, 1], 0.25), ([0, 2], 0.25)] diff --git a/cython/test/test_bottleneck_distance.py b/cython/test/test_bottleneck_distance.py deleted file mode 100755 index 4eb5848f..00000000 --- a/cython/test/test_bottleneck_distance.py +++ /dev/null @@ -1,35 +0,0 @@ -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_basic_bottleneck(): - diag1 = [[2.7, 3.7],[9.6, 14.],[34.2, 34.974], [3.,float('Inf')]] - diag2 = [[2.8, 4.45],[9.5, 14.1],[3.2,float('Inf')]] - - assert(gudhi.bottleneck_distance(diag1, diag2, 0.1) == 0.8081763781405569) - assert(gudhi.bottleneck_distance(diag1, diag2) == 0.75) diff --git a/cython/test/test_cover_complex.py b/cython/test/test_cover_complex.py deleted file mode 100755 index 58935264..00000000 --- a/cython/test/test_cover_complex.py +++ /dev/null @@ -1,92 +0,0 @@ -from gudhi import CoverComplex - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2018 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2018 Inria" -__license__ = "GPL v3" - - -def test_empty_constructor(): - # Try to create an empty CoverComplex - cover = CoverComplex() - assert cover.__is_defined() == True - -def test_non_existing_file_read(): - # Try to open a non existing file - cover = CoverComplex() - assert (cover.read_point_cloud('pouetpouettralala.toubiloubabdou') == False) - -def test_files_creation(): - # Create test file - cloud_file = open('cloud', 'w') - cloud_file.write('nOFF\n3\n3 0 0\n0 0 0\n2 1 0\n4 0 0') - cloud_file.close() - cover_file = open('cover', 'w') - cover_file.write('1\n2\n3') - cover_file.close() - graph_file = open('graph', 'w') - graph_file.write('0 1\n0 2\n1 2') - graph_file.close() - -def test_nerve(): - nerve = CoverComplex() - nerve.set_type('Nerve') - assert (nerve.read_point_cloud('cloud') == True) - nerve.set_color_from_coordinate() - nerve.set_graph_from_file('graph') - nerve.set_cover_from_file('cover') - nerve.find_simplices() - stree = nerve.create_simplex_tree() - - assert (stree.num_vertices() == 3) - assert ((stree.num_simplices() - stree.num_vertices()) == 0) - assert (stree.dimension() == 0) - -def test_graph_induced_complex(): - gic = CoverComplex() - gic.set_type('GIC') - assert (gic.read_point_cloud('cloud') == True) - gic.set_color_from_coordinate() - gic.set_graph_from_file('graph') - gic.set_cover_from_file('cover') - gic.find_simplices() - stree = gic.create_simplex_tree() - - assert (stree.num_vertices() == 3) - assert ((stree.num_simplices() - stree.num_vertices()) == 4) - assert (stree.dimension() == 2) - -def test_voronoi_graph_induced_complex(): - gic = CoverComplex() - gic.set_type('GIC') - assert (gic.read_point_cloud('cloud') == True) - gic.set_color_from_coordinate() - gic.set_graph_from_file('graph') - gic.set_cover_from_Voronoi(2) - gic.find_simplices() - stree = gic.create_simplex_tree() - - assert (stree.num_vertices() == 2) - assert ((stree.num_simplices() - stree.num_vertices()) == 1) - assert (stree.dimension() == 1) diff --git a/cython/test/test_cubical_complex.py b/cython/test/test_cubical_complex.py deleted file mode 100755 index 92e591e9..00000000 --- a/cython/test/test_cubical_complex.py +++ /dev/null @@ -1,97 +0,0 @@ -from gudhi import CubicalComplex - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_empty_constructor(): - # Try to create an empty CubicalComplex - cub = CubicalComplex() - assert cub.__is_defined() == False - assert cub.__is_persistence_defined() == False - -def test_non_existing_perseus_file_constructor(): - # Try to open a non existing file - cub = CubicalComplex(perseus_file='pouetpouettralala.toubiloubabdou') - assert cub.__is_defined() == False - assert cub.__is_persistence_defined() == False - -def test_dimension_or_perseus_file_constructor(): - # Create test file - test_file = open('CubicalOneSphere.txt', 'w') - test_file.write('2\n3\n3\n0\n0\n0\n0\n100\n0\n0\n0\n0\n') - test_file.close() - # CubicalComplex can be constructed from dimensions and - # top_dimensional_cells OR from a Perseus-style file name. - cub = CubicalComplex(dimensions=[3, 3], - top_dimensional_cells = [1,2,3,4,5,6,7,8,9], - perseus_file='CubicalOneSphere.txt') - assert cub.__is_defined() == False - assert cub.__is_persistence_defined() == False - - cub = CubicalComplex(top_dimensional_cells = [1,2,3,4,5,6,7,8,9], - perseus_file='CubicalOneSphere.txt') - assert cub.__is_defined() == False - assert cub.__is_persistence_defined() == False - - cub = CubicalComplex(dimensions=[3, 3], - perseus_file='CubicalOneSphere.txt') - assert cub.__is_defined() == False - assert cub.__is_persistence_defined() == False - -def test_dimension_simple_constructor(): - cub = CubicalComplex(dimensions=[3, 3], - top_dimensional_cells = [1,2,3,4,5,6,7,8,9]) - assert cub.__is_defined() == True - assert cub.__is_persistence_defined() == False - assert cub.persistence() == [(0, (1.0, float('inf')))] - assert cub.__is_persistence_defined() == True - assert cub.betti_numbers() == [1, 0, 0] - assert cub.persistent_betti_numbers(0, 1000) == [0, 0, 0] - -def test_user_case_simple_constructor(): - cub = CubicalComplex(dimensions=[3, 3], - top_dimensional_cells = [float('inf'), 0.,0.,0.,1.,0.,0.,0.,0.]) - assert cub.__is_defined() == True - assert cub.__is_persistence_defined() == False - assert cub.persistence() == [(1, (0.0, 1.0)), (0, (0.0, float('inf')))] - assert cub.__is_persistence_defined() == True - other_cub = CubicalComplex(dimensions=[3, 3], - top_dimensional_cells = [1000., 0.,0.,0.,1.,0.,0.,0.,0.]) - assert other_cub.persistence() == [(1, (0.0, 1.0)), (0, (0.0, float('inf')))] - -def test_dimension_file_constructor(): - # Create test file - test_file = open('CubicalOneSphere.txt', 'w') - test_file.write('2\n3\n3\n0\n0\n0\n0\n100\n0\n0\n0\n0\n') - test_file.close() - cub = CubicalComplex(perseus_file='CubicalOneSphere.txt') - assert cub.__is_defined() == True - assert cub.__is_persistence_defined() == False - assert cub.persistence() == [(1, (0.0, 100.0)), (0, (0.0, float('inf')))] - assert cub.__is_persistence_defined() == True - assert cub.betti_numbers() == [1, 0, 0] - assert cub.persistent_betti_numbers(0, 1000) == [1, 0, 0] diff --git a/cython/test/test_euclidean_witness_complex.py b/cython/test/test_euclidean_witness_complex.py deleted file mode 100755 index 2f77210a..00000000 --- a/cython/test/test_euclidean_witness_complex.py +++ /dev/null @@ -1,71 +0,0 @@ -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_empty_euclidean_witness_complex(): - euclidean_witness = gudhi.EuclideanWitnessComplex() - assert euclidean_witness.__is_defined() == False - -def test_witness_complex(): - point_cloud = [[1.0, 1.0], [7.0, 0.0], [4.0, 6.0], [9.0, 6.0], - [0.0, 14.0], [2.0, 19.0], [9.0, 17.0]] - landmarks = [[1.0, 1.0], [7.0, 0.0], [4.0, 6.0]] - euclidean_witness_complex = gudhi.EuclideanWitnessComplex(landmarks=landmarks, witnesses = point_cloud) - simplex_tree = euclidean_witness_complex.create_simplex_tree(max_alpha_square=4.1) - - assert landmarks[0] == euclidean_witness_complex.get_point(0) - assert landmarks[1] == euclidean_witness_complex.get_point(1) - assert landmarks[2] == euclidean_witness_complex.get_point(2) - - assert simplex_tree.get_filtration() == [([0], 0.0), ([1], 0.0), - ([0, 1], 0.0), ([2], 0.0), ([0, 2], 0.0), ([1, 2], 0.0), - ([0, 1, 2], 0.0)] - -def test_empty_euclidean_strong_witness_complex(): - euclidean_strong_witness = gudhi.EuclideanStrongWitnessComplex() - assert euclidean_strong_witness.__is_defined() == False - -def test_strong_witness_complex(): - point_cloud = [[1.0, 1.0], [7.0, 0.0], [4.0, 6.0], [9.0, 6.0], - [0.0, 14.0], [2.0, 19.0], [9.0, 17.0]] - landmarks = [[1.0, 1.0], [7.0, 0.0], [4.0, 6.0]] - euclidean_strong_witness_complex = gudhi.EuclideanStrongWitnessComplex(landmarks=landmarks, witnesses = point_cloud) - simplex_tree = euclidean_strong_witness_complex.create_simplex_tree(max_alpha_square=14.9) - - assert landmarks[0] == euclidean_strong_witness_complex.get_point(0) - assert landmarks[1] == euclidean_strong_witness_complex.get_point(1) - assert landmarks[2] == euclidean_strong_witness_complex.get_point(2) - - assert simplex_tree.get_filtration() == [([0], 0.0), ([1], 0.0), ([2], 0.0)] - - simplex_tree = euclidean_strong_witness_complex.create_simplex_tree(max_alpha_square=100.0) - - assert simplex_tree.get_filtration() == [([0], 0.0), ([1], 0.0), - ([2], 0.0), ([1, 2], 15.0), ([0, 2], 34.0), ([0, 1], 37.0), - ([0, 1, 2], 37.0)] - diff --git a/cython/test/test_reader_utils.py b/cython/test/test_reader_utils.py deleted file mode 100755 index b240c84f..00000000 --- a/cython/test/test_reader_utils.py +++ /dev/null @@ -1,88 +0,0 @@ -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2017 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2017 Inria" -__license__ = "GPL v3" - - -def test_non_existing_csv_file(): - # Try to open a non existing file - matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file='pouetpouettralala.toubiloubabdou') - assert matrix == [] - -def test_full_square_distance_matrix_csv_file(): - # Create test file - test_file = open('full_square_distance_matrix.csv', 'w') - test_file.write('0;1;2;3;\n1;0;4;5;\n2;4;0;6;\n3;5;6;0;') - test_file.close() - matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file="full_square_distance_matrix.csv") - assert matrix == [[], [1.0], [2.0, 4.0], [3.0, 5.0, 6.0]] - -def test_lower_triangular_distance_matrix_csv_file(): - # Create test file - test_file = open('lower_triangular_distance_matrix.csv', 'w') - test_file.write('\n1,\n2,3,\n4,5,6,\n7,8,9,10,') - test_file.close() - matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file="lower_triangular_distance_matrix.csv", separator=",") - assert matrix == [[], [1.0], [2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0]] - -def test_non_existing_persistence_file(): - # Try to open a non existing file - persistence = gudhi.read_persistence_intervals_grouped_by_dimension(persistence_file='pouetpouettralala.toubiloubabdou') - assert persistence == [] - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='pouetpouettralala.toubiloubabdou', only_this_dim=1) - assert persistence == [] - -def test_read_persistence_intervals_without_dimension(): - # Create test file - test_file = open('persistence_intervals_without_dimension.pers', 'w') - test_file.write('# Simple persistence diagram without dimension\n2.7 3.7\n9.6 14.\n34.2 34.974\n3. inf') - test_file.close() - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_without_dimension.pers') - assert persistence == [(2.7, 3.7), (9.6, 14.), (34.2, 34.974), (3., float('Inf'))] - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_without_dimension.pers', only_this_dim=0) - assert persistence == [] - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_without_dimension.pers', only_this_dim=1) - assert persistence == [] - persistence = gudhi.read_persistence_intervals_grouped_by_dimension(persistence_file='persistence_intervals_without_dimension.pers') - assert persistence == {-1: [(2.7, 3.7), (9.6, 14.0), (34.2, 34.974), (3.0, float('Inf'))]} - -def test_read_persistence_intervals_with_dimension(): - # Create test file - test_file = open('persistence_intervals_with_dimension.pers', 'w') - test_file.write('# Simple persistence diagram with dimension\n0 2.7 3.7\n1 9.6 14.\n3 34.2 34.974\n1 3. inf') - test_file.close() - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers') - assert persistence == [(2.7, 3.7), (9.6, 14.), (34.2, 34.974), (3., float('Inf'))] - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=0) - assert persistence == [(2.7, 3.7)] - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=1) - assert persistence == [(9.6, 14.), (3., float('Inf'))] - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=2) - assert persistence == [] - persistence = gudhi.read_persistence_intervals_in_dimension(persistence_file='persistence_intervals_with_dimension.pers', only_this_dim=3) - assert persistence == [(34.2, 34.974)] - persistence = gudhi.read_persistence_intervals_grouped_by_dimension(persistence_file='persistence_intervals_with_dimension.pers') - assert persistence == {0: [(2.7, 3.7)], 1: [(9.6, 14.0), (3.0, float('Inf'))], 3: [(34.2, 34.974)]} diff --git a/cython/test/test_rips_complex.py b/cython/test/test_rips_complex.py deleted file mode 100755 index c37b5400..00000000 --- a/cython/test/test_rips_complex.py +++ /dev/null @@ -1,111 +0,0 @@ -from gudhi import RipsComplex -from math import sqrt - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_empty_rips(): - rips_complex = RipsComplex() - assert rips_complex.__is_defined() == True - -def test_rips_from_points(): - point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - rips_complex = RipsComplex(points=point_list, max_edge_length=42) - - simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) - - assert simplex_tree.__is_defined() == True - assert simplex_tree.__is_persistence_defined() == False - - assert simplex_tree.num_simplices() == 10 - assert simplex_tree.num_vertices() == 4 - - assert simplex_tree.get_filtration() == \ - [([0], 0.0), ([1], 0.0), ([2], 0.0), ([3], 0.0), - ([0, 1], 1.0), ([0, 2], 1.0), ([1, 3], 1.0), - ([2, 3], 1.0), ([1, 2], 1.4142135623730951), - ([0, 3], 1.4142135623730951)] - assert simplex_tree.get_star([0]) == \ - [([0], 0.0), ([0, 1], 1.0), ([0, 2], 1.0), - ([0, 3], 1.4142135623730951)] - assert simplex_tree.get_cofaces([0], 1) == \ - [([0, 1], 1.0), ([0, 2], 1.0), - ([0, 3], 1.4142135623730951)] - -def test_filtered_rips_from_points(): - point_list = [[0, 0], [1, 0], [0, 1], [1, 1]] - filtered_rips = RipsComplex(points=point_list, max_edge_length=1.0) - - simplex_tree = filtered_rips.create_simplex_tree(max_dimension=1) - - assert simplex_tree.__is_defined() == True - assert simplex_tree.__is_persistence_defined() == False - - assert simplex_tree.num_simplices() == 8 - assert simplex_tree.num_vertices() == 4 - -def test_rips_from_distance_matrix(): - distance_matrix = [[0], - [1, 0], - [1, sqrt(2), 0], - [sqrt(2), 1, 1, 0]] - rips_complex = RipsComplex(distance_matrix=distance_matrix, max_edge_length=42) - - simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) - - assert simplex_tree.__is_defined() == True - assert simplex_tree.__is_persistence_defined() == False - - assert simplex_tree.num_simplices() == 10 - assert simplex_tree.num_vertices() == 4 - - assert simplex_tree.get_filtration() == \ - [([0], 0.0), ([1], 0.0), ([2], 0.0), ([3], 0.0), - ([0, 1], 1.0), ([0, 2], 1.0), ([1, 3], 1.0), - ([2, 3], 1.0), ([1, 2], 1.4142135623730951), - ([0, 3], 1.4142135623730951)] - assert simplex_tree.get_star([0]) == \ - [([0], 0.0), ([0, 1], 1.0), ([0, 2], 1.0), - ([0, 3], 1.4142135623730951)] - assert simplex_tree.get_cofaces([0], 1) == \ - [([0, 1], 1.0), ([0, 2], 1.0), - ([0, 3], 1.4142135623730951)] - -def test_filtered_rips_from_distance_matrix(): - distance_matrix = [[0], - [1, 0], - [1, sqrt(2), 0], - [sqrt(2), 1, 1, 0]] - filtered_rips = RipsComplex(distance_matrix=distance_matrix, max_edge_length=1.0) - - simplex_tree = filtered_rips.create_simplex_tree(max_dimension=1) - - assert simplex_tree.__is_defined() == True - assert simplex_tree.__is_persistence_defined() == False - - assert simplex_tree.num_simplices() == 8 - assert simplex_tree.num_vertices() == 4 diff --git a/cython/test/test_simplex_tree.py b/cython/test/test_simplex_tree.py deleted file mode 100755 index cb701c9a..00000000 --- a/cython/test/test_simplex_tree.py +++ /dev/null @@ -1,207 +0,0 @@ -from gudhi import SimplexTree - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_insertion(): - st = SimplexTree() - assert st.__is_defined() == True - assert st.__is_persistence_defined() == False - - # insert test - assert st.insert([0, 1]) == True - - assert st.dimension() == 1 - - assert st.insert([0, 1, 2], filtration=4.0) == True - - assert st.dimension() == 2 - - assert st.num_simplices() == 7 - assert st.num_vertices() == 3 - - # find test - assert st.find([0, 1, 2]) == True - assert st.find([0, 1]) == True - assert st.find([0, 2]) == True - assert st.find([0]) == True - assert st.find([1]) == True - assert st.find([2]) == True - assert st.find([3]) == False - assert st.find([0, 3]) == False - assert st.find([1, 3]) == False - assert st.find([2, 3]) == False - - # filtration test - st.initialize_filtration() - assert st.filtration([0, 1, 2]) == 4.0 - assert st.filtration([0, 2]) == 4.0 - assert st.filtration([1, 2]) == 4.0 - assert st.filtration([2]) == 4.0 - assert st.filtration([0, 1]) == 0.0 - assert st.filtration([0]) == 0.0 - assert st.filtration([1]) == 0.0 - - # skeleton test - assert st.get_skeleton(2) == \ - [([0, 1, 2], 4.0), ([0, 1], 0.0), ([0, 2], 4.0), - ([0], 0.0), ([1, 2], 4.0), ([1], 0.0), ([2], 4.0)] - assert st.get_skeleton(1) == \ - [([0, 1], 0.0), ([0, 2], 4.0), ([0], 0.0), - ([1, 2], 4.0), ([1], 0.0), ([2], 4.0)] - assert st.get_skeleton(0) == \ - [([0], 0.0), ([1], 0.0), ([2], 4.0)] - - # remove_maximal_simplex test - assert st.get_cofaces([0, 1, 2], 1) == [] - st.remove_maximal_simplex([0, 1, 2]) - assert st.get_skeleton(2) == \ - [([0, 1], 0.0), ([0, 2], 4.0), ([0], 0.0), - ([1, 2], 4.0), ([1], 0.0), ([2], 4.0)] - assert st.find([0, 1, 2]) == False - assert st.find([0, 1]) == True - assert st.find([0, 2]) == True - assert st.find([0]) == True - assert st.find([1]) == True - assert st.find([2]) == True - - st.initialize_filtration() - assert st.persistence(persistence_dim_max = True) == [(1, (4.0, float('inf'))), (0, (0.0, float('inf')))] - assert st.__is_persistence_defined() == True - - assert st.betti_numbers() == [1, 1] - assert st.persistent_betti_numbers(-0.1, 10000.0) == [0, 0] - assert st.persistent_betti_numbers(0.0, 10000.0) == [1, 0] - assert st.persistent_betti_numbers(3.9, 10000.0) == [1, 0] - assert st.persistent_betti_numbers(4.0, 10000.0) == [1, 1] - assert st.persistent_betti_numbers(9999.0, 10000.0) == [1, 1] - -def test_expansion(): - st = SimplexTree() - assert st.__is_defined() == True - assert st.__is_persistence_defined() == False - - # insert test - assert st.insert([3, 2], 0.1) == True - assert st.insert([2, 0], 0.2) == True - assert st.insert([1, 0], 0.3) == True - assert st.insert([3, 1], 0.4) == True - assert st.insert([2, 1], 0.5) == True - assert st.insert([6, 5], 0.6) == True - assert st.insert([4, 2], 0.7) == True - assert st.insert([3, 0], 0.8) == True - assert st.insert([6, 4], 0.9) == True - assert st.insert([6, 3], 1.0) == True - - assert st.num_vertices() == 7 - assert st.num_simplices() == 17 - assert st.get_filtration() == [([2], 0.1), ([3], 0.1), ([2, 3], 0.1), - ([0], 0.2), ([0, 2], 0.2), ([1], 0.3), ([0, 1], 0.3), ([1, 3], 0.4), - ([1, 2], 0.5), ([5], 0.6), ([6], 0.6), ([5, 6], 0.6), ([4], 0.7), - ([2, 4], 0.7), ([0, 3], 0.8), ([4, 6], 0.9), ([3, 6], 1.0)] - - st.expansion(3) - assert st.num_vertices() == 7 - assert st.num_simplices() == 22 - st.initialize_filtration() - - assert st.get_filtration() == [([2], 0.1), ([3], 0.1), ([2, 3], 0.1), - ([0], 0.2), ([0, 2], 0.2), ([1], 0.3), ([0, 1], 0.3), ([1, 3], 0.4), - ([1, 2], 0.5), ([0, 1, 2], 0.5), ([1, 2, 3], 0.5), ([5], 0.6), ([6], 0.6), - ([5, 6], 0.6), ([4], 0.7), ([2, 4], 0.7), ([0, 3], 0.8), ([0, 1, 3], 0.8), - ([0, 2, 3], 0.8), ([0, 1, 2, 3], 0.8), ([4, 6], 0.9), ([3, 6], 1.0)] - -def test_automatic_dimension(): - st = SimplexTree() - assert st.__is_defined() == True - assert st.__is_persistence_defined() == False - - # insert test - assert st.insert([0,1,3], filtration=0.5) == True - assert st.insert([0,1,2], filtration=1.) == True - - assert st.num_vertices() == 4 - assert st.num_simplices() == 11 - - assert st.dimension() == 2 - assert st.upper_bound_dimension() == 2 - - assert st.prune_above_filtration(0.6) == True - assert st.dimension() == 2 - assert st.upper_bound_dimension() == 2 - - st.assign_filtration([0, 1, 3], 0.7) - assert st.filtration([0, 1, 3]) == 0.7 - - st.remove_maximal_simplex([0, 1, 3]) - assert st.upper_bound_dimension() == 2 - assert st.dimension() == 1 - assert st.upper_bound_dimension() == 1 - -def test_make_filtration_non_decreasing(): - st = SimplexTree() - assert st.__is_defined() == True - assert st.__is_persistence_defined() == False - - # Inserted simplex: - # 1 - # o - # /X\ - # o---o---o---o - # 2 0 3\X/4 - # o - # 5 - assert st.insert([2, 1, 0], filtration=2.0) == True - assert st.insert([3, 0], filtration=2.0) == True - assert st.insert([3, 4, 5], filtration=2.0) == True - - assert st.make_filtration_non_decreasing() == False - - # Because of non decreasing property of simplex tree, { 0 } , { 1 } and - # { 0, 1 } are going to be set from value 2.0 to 1.0 - st.insert([0, 1, 6, 7], filtration=1.0); - - assert st.make_filtration_non_decreasing() == False - - # Modify specific values to test make_filtration_non_decreasing - st.assign_filtration([0,1,6,7], 0.8); - st.assign_filtration([0,1,6], 0.9); - st.assign_filtration([0,6], 0.6); - st.assign_filtration([3,4,5], 1.2); - st.assign_filtration([3,4], 1.1); - st.assign_filtration([4,5], 1.99); - - assert st.make_filtration_non_decreasing() == True - - assert st.filtration([0,1,6,7]) == 1. - assert st.filtration([0,1,6]) == 1. - assert st.filtration([0,1]) == 1. - assert st.filtration([0]) == 1. - assert st.filtration([1]) == 1. - assert st.filtration([3,4,5]) == 2. - assert st.filtration([3,4]) == 2. - assert st.filtration([4,5]) == 2. diff --git a/cython/test/test_subsampling.py b/cython/test/test_subsampling.py deleted file mode 100755 index 96906a6f..00000000 --- a/cython/test/test_subsampling.py +++ /dev/null @@ -1,133 +0,0 @@ -import gudhi - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_write_off_file_for_tests(): - file = open("subsample.off", "w") - file.write("nOFF\n") - file.write("2 7 0 0\n") - file.write("1.0 1.0\n") - file.write("7.0 0.0\n") - file.write("4.0 6.0\n") - file.write("9.0 6.0\n") - file.write("0.0 14.0\n") - file.write("2.0 19.0\n") - file.write("9.0 17.0\n") - file.close() - -def test_simple_choose_n_farthest_points_with_a_starting_point(): - point_set = [[0,1], [0,0], [1,0], [1,1]] - i = 0 - for point in point_set: - # The iteration starts with the given starting point - sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 1, starting_point = i) - assert sub_set[0] == point_set[i] - i = i + 1 - - # The iteration finds then the farthest - sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 1) - assert sub_set[1] == point_set[3] - sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 3) - assert sub_set[1] == point_set[1] - sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 0) - assert sub_set[1] == point_set[2] - sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = 2, starting_point = 2) - assert sub_set[1] == point_set[0] - - # Test the limits - assert gudhi.choose_n_farthest_points(points = [], nb_points = 0, starting_point = 0) == [] - assert gudhi.choose_n_farthest_points(points = [], nb_points = 1, starting_point = 0) == [] - assert gudhi.choose_n_farthest_points(points = [], nb_points = 0, starting_point = 1) == [] - assert gudhi.choose_n_farthest_points(points = [], nb_points = 1, starting_point = 1) == [] - - # From off file test - for i in range (0, 7): - assert len(gudhi.choose_n_farthest_points(off_file = 'subsample.off', nb_points = i, starting_point = i)) == i - -def test_simple_choose_n_farthest_points_randomed(): - point_set = [[0,1], [0,0], [1,0], [1,1]] - # Test the limits - assert gudhi.choose_n_farthest_points(points = [], nb_points = 0) == [] - assert gudhi.choose_n_farthest_points(points = [], nb_points = 1) == [] - assert gudhi.choose_n_farthest_points(points = point_set, nb_points = 0) == [] - - # Go furter than point set on purpose - for iter in range(1,10): - sub_set = gudhi.choose_n_farthest_points(points = point_set, nb_points = iter) - for sub in sub_set: - found = False - for point in point_set: - if point == sub: - found = True - # Check each sub set point is existing in the point set - assert found == True - - # From off file test - for i in range (0, 7): - assert len(gudhi.choose_n_farthest_points(off_file = 'subsample.off', nb_points = i)) == i - -def test_simple_pick_n_random_points(): - point_set = [[0,1], [0,0], [1,0], [1,1]] - # Test the limits - assert gudhi.pick_n_random_points(points = [], nb_points = 0) == [] - assert gudhi.pick_n_random_points(points = [], nb_points = 1) == [] - assert gudhi.pick_n_random_points(points = point_set, nb_points = 0) == [] - - # Go furter than point set on purpose - for iter in range(1,10): - sub_set = gudhi.pick_n_random_points(points = point_set, nb_points = iter) - print(5) - for sub in sub_set: - found = False - for point in point_set: - if point == sub: - found = True - # Check each sub set point is existing in the point set - assert found == True - - # From off file test - for i in range (0, 7): - assert len(gudhi.pick_n_random_points(off_file = 'subsample.off', nb_points = i)) == i - -def test_simple_sparsify_points(): - point_set = [[0,1], [0,0], [1,0], [1,1]] - # Test the limits - # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 0.0) == [] - # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 10.0) == [] - assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 0.0) == point_set - assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 1.0) == point_set - assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 2.0) == [[0,1], [1,0]] - assert gudhi.sparsify_point_set(points = point_set, min_squared_dist = 2.01) == [[0,1]] - - assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 0.0)) == 7 - assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 30.0)) == 5 - assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 40.0)) == 4 - assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 90.0)) == 3 - assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 100.0)) == 2 - assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 325.0)) == 2 - assert len(gudhi.sparsify_point_set(off_file = 'subsample.off', min_squared_dist = 325.01)) == 1 diff --git a/cython/test/test_tangential_complex.py b/cython/test/test_tangential_complex.py deleted file mode 100755 index 5385a0d3..00000000 --- a/cython/test/test_tangential_complex.py +++ /dev/null @@ -1,52 +0,0 @@ -from gudhi import TangentialComplex, SimplexTree - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_tangential(): - point_list = [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]] - tc = TangentialComplex(intrisic_dim = 1, points=point_list) - assert tc.__is_defined() == True - assert tc.num_vertices() == 4 - - st = tc.create_simplex_tree() - assert st.__is_defined() == True - assert st.__is_persistence_defined() == False - - assert st.num_simplices() == 6 - assert st.num_vertices() == 4 - - assert st.get_filtration() == \ - [([0], 0.0), ([1], 0.0), ([2], 0.0), ([0, 2], 0.0), ([3], 0.0), ([1, 3], 0.0)] - assert st.get_cofaces([0], 1) == [([0, 2], 0.0)] - - assert point_list[0] == tc.get_point(0) - assert point_list[1] == tc.get_point(1) - assert point_list[2] == tc.get_point(2) - assert point_list[3] == tc.get_point(3) - assert tc.get_point(4) == [] - assert tc.get_point(125) == [] diff --git a/cython/test/test_witness_complex.py b/cython/test/test_witness_complex.py deleted file mode 100755 index bcbc521b..00000000 --- a/cython/test/test_witness_complex.py +++ /dev/null @@ -1,62 +0,0 @@ -from gudhi import WitnessComplex, StrongWitnessComplex, SimplexTree - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2016 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - - -def test_empty_witness_complex(): - witness = WitnessComplex() - assert witness.__is_defined() == False - -def test_witness_complex(): - nearest_landmark_table = [[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]], - [[1, 0], [2, 1], [3, 2], [4, 3], [0, 4]], - [[2, 0], [3, 1], [4, 2], [0, 3], [1, 4]], - [[3, 0], [4, 1], [0, 2], [1, 3], [2, 4]], - [[4, 0], [0, 1], [1, 2], [2, 3], [3, 4]]] - - witness_complex = WitnessComplex(nearest_landmark_table=nearest_landmark_table) - simplex_tree = witness_complex.create_simplex_tree(max_alpha_square=4.1) - assert simplex_tree.num_vertices() == 5 - assert simplex_tree.num_simplices() == 31 - simplex_tree = witness_complex.create_simplex_tree(max_alpha_square=4.1, limit_dimension=2) - assert simplex_tree.num_vertices() == 5 - assert simplex_tree.num_simplices() == 25 - -def test_strong_witness_complex(): - nearest_landmark_table = [[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]], - [[1, 0], [2, 1], [3, 2], [4, 3], [0, 4]], - [[2, 0], [3, 1], [4, 2], [0, 3], [1, 4]], - [[3, 0], [4, 1], [0, 2], [1, 3], [2, 4]], - [[4, 0], [0, 1], [1, 2], [2, 3], [3, 4]]] - - strong_witness_complex = StrongWitnessComplex(nearest_landmark_table=nearest_landmark_table) - simplex_tree = strong_witness_complex.create_simplex_tree(max_alpha_square=4.1) - assert simplex_tree.num_vertices() == 5 - assert simplex_tree.num_simplices() == 31 - simplex_tree = strong_witness_complex.create_simplex_tree(max_alpha_square=4.1, limit_dimension=2) - assert simplex_tree.num_vertices() == 5 - assert simplex_tree.num_simplices() == 25 |
