koln_xcorr_networks

our_script.py

#!usr/bin/env python

import numpy as np
import pyunicorn
import matplotlib.pyplot as pl

'''
# make a big bad ass array with all the subjects
for subj in range(1, 11):
    print '>Running for subject %d' %(subj)
    data = np.loadtxt('fmri_subject_%d.txt' % subj)
    big_data[subj - 1] = data.T
    np.save('all_fmri_data.npy')
'''

# reading our data
data = np.loadtxt('all_fmri_data.npy')

def prepare_xcorr_network(data, thresh_val=95, save_name='test.gml'):
    ''' Read data and save the network as gml. '''
    xcorr = np.corrcoef(data.T)

    # performing cross correlation
    xcorr = np.abs(xcorr)
    xcorr = xcorr - np.eye(len(xcorr))
    print 'Maximum value is %f and minimum value is %f' % (xcorr.min(), xcorr.max())

    # set the threshold percentile
    thresh = np.percentile(xcorr, thresh_val)

    # binary matrix
    xcorr_bin = xcorr >= thresh

    link_weights = np.abs(xcorr_bin)
    channel_names = range(0, xcorr.shape[0])
    channel_names = [str(i) for i in channel_names]

    # initialiying the network
    net = pyunicorn.Network(xcorr_bin)

    net.set_link_attribute(attribute_name="weight", values=link_weights)
    net.set_node_attribute(attribute_name="label", values=channel_names)

    if save_name:
        print 'Saving the file...'
        net.save(save_name)  # save the network

for subj in range(1, 11):
    print '>Running for subject %d' %(subj)
    data = np.loadtxt('fmri_subject_%d.txt' % subj)
    print data.shape
    print ''
    prepare_xcorr_network(data, thresh_val=95, save_name=None)
    #prepare_xcorr_network(data, thresh_val=95, save_name='fmri_subj_%d.gml' % subj)

#plotting the image
#pl.imshow(xcorr)
#pl.show()