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#!/usr/bin/env python
import gudhi
import pandas
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) 2016 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) 2016 INRIA"
__license__ = "GPL v3"
print("#####################################################################")
print("WitnessComplex creation from points read in a file")
parser = argparse.ArgumentParser(description='WitnessComplex creation from '
'points read in a file.',
epilog='Example: '
'example/witness_complex_from_file_example.py'
' data/2000_random_points_on_3D_Torus.csv '
'- Constructs a witness complex with the '
'points from the given file. File format '
'is X1, X2, ..., Xn')
parser.add_argument('file', type=argparse.FileType('r'))
args = parser.parse_args()
points = pandas.read_csv(args.file, header=None)
print("WitnessComplex with number_of_landmarks=100")
witness_complex = gudhi.WitnessComplex(points=points.values,
number_of_landmarks=100)
witness_complex.initialize_filtration()
print("filtered_tree=", witness_complex.get_filtered_tree())
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