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Diffstat (limited to 'cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py')
-rwxr-xr-x | cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py | 84 |
1 files changed, 0 insertions, 84 deletions
diff --git a/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py b/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py deleted file mode 100755 index 0c9dfc43..00000000 --- a/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python - -import gudhi -import sys -import argparse - -"""This file is part of the Gudhi Library. The Gudhi library - (Geometric Understanding in Higher Dimensions) is a generic C++ - library for computational topology. - - Author(s): Vincent Rouvreau - - Copyright (C) 2017 Inria - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2017 Inria" -__license__ = "GPL v3" - -parser = argparse.ArgumentParser(description='RipsComplex creation from ' - 'a correlation matrix read in a csv file.', - epilog='Example: ' - 'example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py ' - '-f ../data/correlation_matrix/lower_triangular_correlation_matrix.csv -e 12.0 -d 3' - '- Constructs a Rips complex with the ' - 'correlation matrix from the given csv file.') -parser.add_argument("-f", "--file", type=str, required=True) -parser.add_argument("-c", "--min_edge_correlation", type=float, default=0.5) -parser.add_argument("-d", "--max_dimension", type=int, default=1) -parser.add_argument("-b", "--band", type=float, default=0.) -parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') - -args = parser.parse_args() - -if not (-1. < args.min_edge_correlation < 1.): - print("Wrong value of the treshold corelation (should be between -1 and 1).") - sys.exit(1) - -print("#####################################################################") -print("Caution: as persistence diagrams points will be under the diagonal,") -print("bottleneck distance and persistence graphical tool will not work") -print("properly, this is a known issue.") - -print("#####################################################################") -print("RipsComplex creation from correlation matrix read in a csv file") - -message = "RipsComplex with min_edge_correlation=" + repr(args.min_edge_correlation) -print(message) - -correlation_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=args.file) -# Given a correlation matrix M, we compute component-wise M'[i,j] = 1-M[i,j] to get a distance matrix: -distance_matrix = [[1.-correlation_matrix[i][j] for j in range(len(correlation_matrix[i]))] for i in range(len(correlation_matrix))] - -rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, - max_edge_length=1.-args.min_edge_correlation) -simplex_tree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) - -message = "Number of simplices=" + repr(simplex_tree.num_simplices()) -print(message) - -diag = simplex_tree.persistence() - -print("betti_numbers()=") -print(simplex_tree.betti_numbers()) - -# invert the persistence diagram -invert_diag = [(diag[pers][0],(1.-diag[pers][1][0], 1.-diag[pers][1][1])) for pers in range(len(diag))] - -if args.no_diagram == False: - pplot = gudhi.plot_persistence_diagram(invert_diag, band=args.band) - pplot.show() |