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#!/usr/bin/env python
import gudhi
import sys
import argparse
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
Author(s): Vincent Rouvreau
Copyright (C) 2017 Inria
Modification(s):
- YYYY/MM Author: Description of the modification
"""
__author__ = "Vincent Rouvreau"
__copyright__ = "Copyright (C) 2017 Inria"
__license__ = "MIT"
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()
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