blob: e83466da9a9f8fc48ae48ffb162e8f4ef1483aa2 (
plain)
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
|
# This file is part of MAMMULT: Metrics And Models for Multilayer Networks
#
# 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/>.
####
##
## Compute the pearson correlation coefficient between the values of
## node properties included in the two files provided as input.
##
import sys
import numpy
import scipy.stats
import math
if len(sys.argv) < 3:
print "Usage %s <file1> <file2>" % sys.argv[0]
sys.exit(1)
x1 = []
with open(sys.argv[1], "r") as lines:
for l in lines:
elem = [float(x) if "." in x or "e" in x else int(x) for x in l.strip(" \n").split()][0]
x1.append(elem)
x2 = []
with open(sys.argv[2], "r") as lines:
for l in lines:
elem = [float(x) if "." in x or "e" in x else int(x) for x in l.strip(" \n").split()][0]
x2.append(elem)
r2 =numpy.corrcoef(x1,x2)
print r2[0][1]
|
