diff options
Diffstat (limited to 'cython')
| -rw-r--r-- | cython/CMakeLists.txt | 228 | ||||
| -rw-r--r-- | cython/cython/nerve_gic.pyx | 401 | ||||
| -rwxr-xr-x | cython/cython/persistence_graphical_tools.py | 377 | ||||
| -rw-r--r-- | cython/cython/rips_complex.pyx | 2 | ||||
| -rw-r--r-- | cython/cython/subsampling.pyx | 8 | ||||
| -rw-r--r-- | cython/doc/_templates/layout.html | 2 | ||||
| -rw-r--r-- | cython/doc/examples.rst | 4 | ||||
| -rw-r--r-- | cython/doc/index.rst | 5 | ||||
| -rw-r--r-- | cython/doc/installation.rst | 54 | ||||
| -rw-r--r-- | cython/doc/nerve_gic_complex_ref.rst | 10 | ||||
| -rw-r--r-- | cython/doc/nerve_gic_complex_sum.rst | 15 | ||||
| -rw-r--r-- | cython/doc/nerve_gic_complex_user.rst | 312 | ||||
| -rwxr-xr-x | cython/example/coordinate_graph_induced_complex.py | 68 | ||||
| -rwxr-xr-x | cython/example/functional_graph_induced_complex.py | 69 | ||||
| -rwxr-xr-x | cython/example/nerve_of_a_covering.py | 70 | ||||
| -rwxr-xr-x | cython/example/voronoi_graph_induced_complex.py | 65 | ||||
| -rw-r--r-- | cython/gudhi.pyx.in | 2 | ||||
| -rw-r--r-- | cython/include/Nerve_gic_interface.h | 61 | ||||
| -rw-r--r-- | cython/setup.py.in | 6 | ||||
| -rwxr-xr-x | cython/test/test_cover_complex.py | 92 |
20 files changed, 1598 insertions, 253 deletions
diff --git a/cython/CMakeLists.txt b/cython/CMakeLists.txt index 17d440ee..09ea28f1 100644 --- a/cython/CMakeLists.txt +++ b/cython/CMakeLists.txt @@ -16,17 +16,51 @@ 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 ) - # 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) + 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) - message("++ ${PYTHON_EXECUTABLE} v.${PYTHON_VERSION_STRING} - Cython is ${CYTHON_EXECUTABLE} - Sphinx is ${SPHINX_PATH}") + set(GUDHI_CYTHON_MODULES "${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', ") @@ -49,49 +83,86 @@ if(CYTHON_FOUND) 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 '${CMAKE_CURRENT_SOURCE_DIR}/cython/bottleneck_distance.pyx'") - endif (NOT CGAL_VERSION VERSION_LESS 4.8.1) + set(GUDHI_CYTHON_MODULES "${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 '${CMAKE_CURRENT_SOURCE_DIR}/cython/tangential_complex.pyx'") - endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + set(GUDHI_CYTHON_MODULES "${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 '${CMAKE_CURRENT_SOURCE_DIR}/cython/alpha_complex.pyx'") - endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.7.0) + set(GUDHI_CYTHON_MODULES "${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 '${CMAKE_CURRENT_SOURCE_DIR}/cython/euclidean_strong_witness_complex.pyx'\n") - endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.6.0) + set(GUDHI_CYTHON_MODULES "${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: ${GUDHI_CYTHON_MISSING_MODULES}") + endif() if(CGAL_FOUND) can_cgal_use_cxx11_thread_local() if (NOT CGAL_CAN_USE_CXX11_THREAD_LOCAL_RESULT) - add_gudhi_cython_lib(${Boost_THREAD_LIBRARY}) + 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_cython_lib(${CGAL_LIBRARY}) + 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, - add_gudhi_cython_lib(${Boost_SYSTEM_LIBRARY}) + if(CMAKE_BUILD_TYPE MATCHES Debug) + add_gudhi_cython_lib("${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(${GMP_LIBRARIES}) + add_gudhi_cython_lib("${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(${GMPXX_LIBRARIES}) + add_gudhi_cython_lib("${GMPXX_LIBRARIES}") set(GUDHI_CYTHON_LIBRARY_DIRS "${GUDHI_CYTHON_LIBRARY_DIRS}'${GMPXX_LIBRARIES_DIR}', ") endif(GMPXX_FOUND) endif(GMP_FOUND) @@ -111,9 +182,15 @@ if(CYTHON_FOUND) 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', ") - add_gudhi_cython_lib(${TBB_RELEASE_LIBRARY}) - add_gudhi_cython_lib(${TBB_MALLOC_RELEASE_LIBRARY}) + 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() @@ -151,40 +228,73 @@ if(CYTHON_FOUND) ${PYTHON_EXECUTABLE} "${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) - # Tangential - add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test + 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/tangential_complex_plain_homology_from_off_file_example.py" - --no-diagram -i 2 -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off) + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/bottleneck_basic_example.py") - add_gudhi_py_test(test_tangential_complex) + add_gudhi_py_test(test_bottleneck_distance) - # Witness complex AND Subsampling - add_test(NAME euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test + # 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/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) + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/nerve_of_a_covering.py" + -f human.off -c 2 -r 10 -g 0.3) - add_test(NAME euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test + 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/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) + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/coordinate_graph_induced_complex.py" + -f human.off -c 0 -v) - # Subsampling - add_gudhi_py_test(test_subsampling) + 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) - 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 + 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/bottleneck_basic_example.py") + ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/voronoi_graph_induced_complex.py" + -f human.off -n 700 -v) - add_gudhi_py_test(test_bottleneck_distance) + 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) @@ -194,11 +304,13 @@ if(CYTHON_FOUND) COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}" ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_from_points_example.py") - add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test + 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) @@ -217,26 +329,30 @@ if(CYTHON_FOUND) ${PYTHON_EXECUTABLE} "${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) - 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) + 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 - 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) + 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} @@ -265,7 +381,7 @@ if(CYTHON_FOUND) add_gudhi_py_test(test_reader_utils) # Documentation generation is available through sphinx - requires all modules - if(SPHINX_PATH AND NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + 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 @@ -285,5 +401,5 @@ if(CYTHON_FOUND) 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(SPHINX_PATH AND NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) + endif() endif(CYTHON_FOUND) diff --git a/cython/cython/nerve_gic.pyx b/cython/cython/nerve_gic.pyx new file mode 100644 index 00000000..30a14d3b --- /dev/null +++ b/cython/cython/nerve_gic.pyx @@ -0,0 +1,401 @@ +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/persistence_graphical_tools.py b/cython/cython/persistence_graphical_tools.py index 216ab8d6..314bd6db 100755 --- a/cython/cython/persistence_graphical_tools.py +++ b/cython/cython/persistence_graphical_tools.py @@ -1,8 +1,3 @@ -import matplotlib.pyplot as plt -import matplotlib.patches as mpatches -import numpy as np -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. @@ -29,187 +24,197 @@ __author__ = "Vincent Rouvreau, Bertrand Michel" __copyright__ = "Copyright (C) 2016 Inria" __license__ = "GPL v3" -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) +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). + Only 13 colors for the palette """ - 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 + 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/rips_complex.pyx b/cython/cython/rips_complex.pyx index 59c16bff..30ca4443 100644 --- a/cython/cython/rips_complex.pyx +++ b/cython/cython/rips_complex.pyx @@ -51,7 +51,7 @@ cdef class RipsComplex: """RipsComplex constructor. :param max_edge_length: Rips value. - :type max_edge_length: int + :type max_edge_length: float :param points: A list of points in d-Dimension. :type points: list of list of double diff --git a/cython/cython/subsampling.pyx b/cython/cython/subsampling.pyx index ac09b7a3..e9d61a37 100644 --- a/cython/cython/subsampling.pyx +++ b/cython/cython/subsampling.pyx @@ -112,7 +112,8 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): return subsampling_n_random_points(points, nb_points) def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): - """Subsample a point set by picking random vertices. + """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]]. @@ -122,8 +123,9 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): :param off_file: An OFF file style name. :type off_file: string - :param min_squared_dist: Number of points of the subsample. - :type min_squared_dist: unsigned. + :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]] """ diff --git a/cython/doc/_templates/layout.html b/cython/doc/_templates/layout.html index 1161ed8e..bc0e9658 100644 --- a/cython/doc/_templates/layout.html +++ b/cython/doc/_templates/layout.html @@ -107,7 +107,7 @@ {%- macro css() %} <!-- GUDHI website css for header BEGIN --> -<link rel="stylesheet" type="text/css" href="http://gudhi.gforge.inria.fr/assets/css/styles_feeling_responsive.css" /> +<link rel="stylesheet" type="text/css" href="https://gudhi.inria.fr/assets/css/styles_feeling_responsive.css" /> <!-- GUDHI website css for header END --> <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" /> <link rel="stylesheet" href="{{ pathto('_static/pygments.css', 1) }}" type="text/css" /> diff --git a/cython/doc/examples.rst b/cython/doc/examples.rst index d42f5a92..1f02f8a2 100644 --- a/cython/doc/examples.rst +++ b/cython/doc/examples.rst @@ -23,3 +23,7 @@ Examples * :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/index.rst b/cython/doc/index.rst index 4e444fb0..15cbe267 100644 --- a/cython/doc/index.rst +++ b/cython/doc/index.rst @@ -36,6 +36,11 @@ Alpha complex .. include:: alpha_complex_sum.inc +Cover complexes +=============== + +.. include:: nerve_gic_complex_sum.rst + Cubical complex =============== diff --git a/cython/doc/installation.rst b/cython/doc/installation.rst index 43ff85c5..43576ec9 100644 --- a/cython/doc/installation.rst +++ b/cython/doc/installation.rst @@ -47,9 +47,61 @@ following command in a terminal: export PYTHONPATH='$PYTHONPATH:/path-to-gudhi/build/cython' ctest -R py_test -If tests fail, please try to :code:`import gudhi` and check the errors. +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 ============= diff --git a/cython/doc/nerve_gic_complex_ref.rst b/cython/doc/nerve_gic_complex_ref.rst new file mode 100644 index 00000000..e24e01fc --- /dev/null +++ b/cython/doc/nerve_gic_complex_ref.rst @@ -0,0 +1,10 @@ +================================ +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 new file mode 100644 index 00000000..72782c7a --- /dev/null +++ b/cython/doc/nerve_gic_complex_sum.rst @@ -0,0 +1,15 @@ +================================================================= =================================== =================================== +: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 new file mode 100644 index 00000000..d774827e --- /dev/null +++ b/cython/doc/nerve_gic_complex_user.rst @@ -0,0 +1,312 @@ +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/example/coordinate_graph_induced_complex.py b/cython/example/coordinate_graph_induced_complex.py new file mode 100755 index 00000000..9e93109a --- /dev/null +++ b/cython/example/coordinate_graph_induced_complex.py @@ -0,0 +1,68 @@ +#!/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/functional_graph_induced_complex.py b/cython/example/functional_graph_induced_complex.py new file mode 100755 index 00000000..6ad7c2ec --- /dev/null +++ b/cython/example/functional_graph_induced_complex.py @@ -0,0 +1,69 @@ +#!/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/nerve_of_a_covering.py b/cython/example/nerve_of_a_covering.py new file mode 100755 index 00000000..c5577cb1 --- /dev/null +++ b/cython/example/nerve_of_a_covering.py @@ -0,0 +1,70 @@ +#!/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/voronoi_graph_induced_complex.py b/cython/example/voronoi_graph_induced_complex.py new file mode 100755 index 00000000..8266a0e4 --- /dev/null +++ b/cython/example/voronoi_graph_induced_complex.py @@ -0,0 +1,65 @@ +#!/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/gudhi.pyx.in b/cython/gudhi.pyx.in index b94f2251..0d4b966b 100644 --- a/cython/gudhi.pyx.in +++ b/cython/gudhi.pyx.in @@ -26,6 +26,7 @@ __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' @@ -41,3 +42,4 @@ include '@CMAKE_CURRENT_SOURCE_DIR@/cython/strong_witness_complex.pyx' @GUDHI_CYTHON_SUBSAMPLING@ @GUDHI_CYTHON_TANGENTIAL_COMPLEX@ @GUDHI_CYTHON_BOTTLENECK_DISTANCE@ +@GUDHI_CYTHON_NERVE_GIC@ diff --git a/cython/include/Nerve_gic_interface.h b/cython/include/Nerve_gic_interface.h new file mode 100644 index 00000000..aa71e2a6 --- /dev/null +++ b/cython/include/Nerve_gic_interface.h @@ -0,0 +1,61 @@ +/* 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/setup.py.in b/cython/setup.py.in index ee381a1b..4037aab6 100644 --- a/cython/setup.py.in +++ b/cython/setup.py.in @@ -46,9 +46,5 @@ setup( version='@GUDHI_VERSION@', url='http://gudhi.gforge.inria.fr/', ext_modules = cythonize(gudhi), - install_requires = [ - "matplotlib", - "numpy", - "cython", - ], + install_requires = ["cython",], ) diff --git a/cython/test/test_cover_complex.py b/cython/test/test_cover_complex.py new file mode 100755 index 00000000..58935264 --- /dev/null +++ b/cython/test/test_cover_complex.py @@ -0,0 +1,92 @@ +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) |