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+#!/usr/bin/env python
+
+import gudhi
+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) 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"
+
+parser = argparse.ArgumentParser(description='Voronoi GIC '
+ 'from points read in a OFF file.',
+ epilog='Example: '
+ 'example/voronoi_graph_induced_complex.py '
+ '-f ../data/points/human.off -n 700 -v'
+ '- Constructs the Voronoi GIC with the '
+ 'points from the given OFF file.')
+parser.add_argument("-f", "--file", type=str, required=True)
+parser.add_argument("-n", "--subsample-nb-points", type=int, default=100)
+parser.add_argument("-v", "--verbose", default=False, action='store_true' , help='Flag for program verbosity')
+
+args = parser.parse_args()
+
+nerve_complex = gudhi.CoverComplex()
+nerve_complex.set_verbose(args.verbose)
+
+if (nerve_complex.read_point_cloud(args.file)):
+ nerve_complex.set_type('GIC')
+ nerve_complex.set_color_from_coordinate()
+ nerve_complex.set_graph_from_OFF()
+ nerve_complex.set_cover_from_Voronoi(args.subsample_nb_points)
+ nerve_complex.find_simplices()
+ nerve_complex.plot_off()
+ simplex_tree = nerve_complex.create_simplex_tree()
+ nerve_complex.compute_PD()
+ if (args.verbose):
+ print('Iterator on graph induced complex simplices')
+ result_str = 'Graph induced complex is of dimension ' + \
+ repr(simplex_tree.dimension()) + ' - ' + \
+ repr(simplex_tree.num_simplices()) + ' simplices - ' + \
+ repr(simplex_tree.num_vertices()) + ' vertices.'
+ print(result_str)
+ for filtered_value in simplex_tree.get_filtration():
+ print(filtered_value[0])