1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
|
#!/usr/bin/env python
import km
import numpy as np
from collections import defaultdict
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): Mathieu Carriere
Copyright (C) 2017 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__ = "Mathieu Carriere"
__copyright__ = "Copyright (C) 2017 INRIA"
__license__ = "GPL v3"
parser = argparse.ArgumentParser(description='Creates an html Keppler Mapper '
'file to visualize a SC.txt file.',
epilog='Example: '
'./KeplerMapperVisuFromTxtFile.py '
'-f ../../data/points/human.off_sc.txt'
'- Constructs an human.off_sc.html file.')
parser.add_argument("-f", "--file", type=str, required=True)
args = parser.parse_args()
with open(args.file, 'r') as f:
network = {}
mapper = km.KeplerMapper(verbose=0)
data = np.zeros((3,3))
projected_data = mapper.fit_transform( data, projection="sum", scaler=None )
nodes = defaultdict(list)
links = defaultdict(list)
custom = defaultdict(list)
dat = f.readline()
lens = f.readline()
color = f.readline();
param = [float(i) for i in f.readline().split(" ")]
nums = [int(i) for i in f.readline().split(" ")]
num_nodes = nums[0]
num_edges = nums[1]
for i in range(0,num_nodes):
point = [float(j) for j in f.readline().split(" ")]
nodes[ str(int(point[0])) ] = [ int(point[0]), point[1], int(point[2]) ]
links[ str(int(point[0])) ] = []
custom[ int(point[0]) ] = point[1]
m = min([custom[i] for i in range(0,num_nodes)])
M = max([custom[i] for i in range(0,num_nodes)])
for i in range(0,num_edges):
edge = [int(j) for j in f.readline().split(" ")]
links[ str(edge[0]) ].append( str(edge[1]) )
links[ str(edge[1]) ].append( str(edge[0]) )
network["nodes"] = nodes
network["links"] = links
network["meta"] = lens
html_output_filename = args.file.rsplit('.', 1)[0] + '.html'
mapper.visualize(network, color_function = color, path_html=html_output_filename, title=dat,
graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0,
height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M)
message = repr(html_output_filename) + " is generated. You can now use your favorite web browser to visualize it."
print(message)
f.close()
|
