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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("RipsComplex creation from points read in a file")
parser = argparse.ArgumentParser(description='RipsComplex creation from '
'points read in a file.',
epilog='Example: '
'example/rips_complex_from_file_example.py '
'data/2000_random_points_on_3D_Torus.csv '
'- Constructs a rips 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("RipsComplex with max_edge_length=0.7")
rips_complex = gudhi.RipsComplex(points=points.values,
max_dimension=len(points.values[0]), max_edge_length=0.7)
rips_complex.initialize_filtration()
diag = rips_complex.persistence(homology_coeff_field=2, min_persistence=0.3)
print("betti_numbers()=")
print(rips_complex.betti_numbers())
gudhi.diagram_persistence(diag)
gudhi.barcode_persistence(diag)
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