diff options
Diffstat (limited to 'cython/cython')
| -rw-r--r-- | cython/cython/alpha_complex.pyx | 121 | ||||
| -rw-r--r-- | cython/cython/bottleneck_distance.pyx | 61 | ||||
| -rw-r--r-- | cython/cython/cubical_complex.pyx | 198 | ||||
| -rw-r--r-- | cython/cython/euclidean_strong_witness_complex.pyx | 97 | ||||
| -rw-r--r-- | cython/cython/euclidean_witness_complex.pyx | 97 | ||||
| -rw-r--r-- | cython/cython/nerve_gic.pyx | 401 | ||||
| -rw-r--r-- | cython/cython/off_reader.pyx | 50 | ||||
| -rw-r--r-- | cython/cython/periodic_cubical_complex.pyx | 200 | ||||
| -rwxr-xr-x | cython/cython/persistence_graphical_tools.py | 220 | ||||
| -rw-r--r-- | cython/cython/reader_utils.pyx | 95 | ||||
| -rw-r--r-- | cython/cython/rips_complex.pyx | 96 | ||||
| -rw-r--r-- | cython/cython/simplex_tree.pyx | 548 | ||||
| -rw-r--r-- | cython/cython/strong_witness_complex.pyx | 81 | ||||
| -rw-r--r-- | cython/cython/subsampling.pyx | 142 | ||||
| -rw-r--r-- | cython/cython/tangential_complex.pyx | 154 | ||||
| -rw-r--r-- | cython/cython/witness_complex.pyx | 81 |
16 files changed, 0 insertions, 2642 deletions
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 |