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
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+++ 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()