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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, 84 insertions, 0 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 new file mode 100755 index 00000000..0c9dfc43 --- /dev/null +++ b/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py @@ -0,0 +1,84 @@ +#!/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() |