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Diffstat (limited to 'cython/cython/nerve_gic.pyx')
| -rw-r--r-- | cython/cython/nerve_gic.pyx | 401 |
1 files changed, 0 insertions, 401 deletions
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() |