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#!/usr/bin/env python
import gudhi as gd
import argparse
import math
import numpy as np
""" 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) 2016 Inria
Modification(s):
- YYYY/MM Author: Description of the modification
"""
__author__ = "Vincent Rouvreau"
__copyright__ = "Copyright (C) 2016 Inria"
__license__ = "MIT"
parser = argparse.ArgumentParser(
description="AlphaComplex and RipsComplex "
"persistence creation from points read in "
"a OFF file. Bottleneck distance computation"
" on each dimension",
epilog="Example: "
"example/alpha_rips_persistence_bottleneck_distance.py "
"-f ../data/points/tore3D_1307.off -t 0.15 -d 3",
)
parser.add_argument("-f", "--file", type=str, required=True)
parser.add_argument("-t", "--threshold", type=float, default=0.5)
parser.add_argument("-d", "--max_dimension", type=int, default=1)
args = parser.parse_args()
point_cloud = gd.read_points_from_off_file(off_file=args.file)
print("##############################################################")
print("RipsComplex creation from points read in a OFF file")
message = "RipsComplex with max_edge_length=" + repr(args.threshold)
print(message)
rips_complex = gd.RipsComplex(
points=point_cloud, max_edge_length=args.threshold
)
rips_stree = rips_complex.create_simplex_tree(
max_dimension=args.max_dimension)
message = "Number of simplices=" + repr(rips_stree.num_simplices())
print(message)
rips_stree.compute_persistence()
print("##############################################################")
print("AlphaComplex creation from points read in a OFF file")
message = "AlphaComplex with max_edge_length=" + repr(args.threshold)
print(message)
alpha_complex = gd.AlphaComplex(points=point_cloud)
alpha_stree = alpha_complex.create_simplex_tree(
max_alpha_square=(args.threshold * args.threshold)
)
message = "Number of simplices=" + repr(alpha_stree.num_simplices())
print(message)
alpha_stree.compute_persistence()
max_b_distance = 0.0
for dim in range(args.max_dimension):
# Alpha persistence values needs to be transform because filtration
# values are alpha square values
alpha_intervals = np.sqrt(alpha_stree.persistence_intervals_in_dimension(dim))
rips_intervals = rips_stree.persistence_intervals_in_dimension(dim)
bottleneck_distance = gd.bottleneck_distance(
rips_intervals, alpha_intervals
)
message = (
"In dimension "
+ repr(dim)
+ ", bottleneck distance = "
+ repr(bottleneck_distance)
)
print(message)
max_b_distance = max(bottleneck_distance, max_b_distance)
print("==============================================================")
message = "Bottleneck distance is " + repr(max_b_distance)
print(message)
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