cliarg_rest = sys.argv[2:]
# list of all subjects as numpy array
subject_list = np.array(cliarg_rest) # e.g. /ptmp/sbayrak/hcp/*
cnt_files = 4
N_user = None
N = len(subject_list)
for i in range(0, N):
subject = subject_list[i]
print "do loop %d/%d, %s" % (i+1, N, subject)
# load time-series matrix of the subject
K = hcp_util.t_series(subject, cnt_files=cnt_files, N_cnt=N_user)
# get upper-triangular of correlation matrix of time-series as 1D array
K = hcp_util.corrcoef_upper(K)
print "corrcoef data shape: ", K.shape
# Fisher r to z transform on the correlation upper triangular
K = fisher_r2z(K)
# sum all Fisher transformed 1D arrays
if i == 0:
SUM = K
else:
SUM = ne.evaluate('SUM + K')
del K
if args.nuser != None:
N_cnt = args.nuser
## end parse command line arguments
# you may override this to make testing faster
cnt_files = 4
N = len(subject_list)
for i in range(0, N):
subject = subject_list[i]
print "do loop %d/%d, %s" % (i+1, N, subject)
# load time-series matrix of the subject
K = hcp_util.t_series(subject, cnt_files=cnt_files,
N_first=N_first, N_cnt=N_cnt)
print K.shape
# get upper-triangular of correlation matrix of time-series as 1D array
K = hcp_util.corrcoef_upper(K)
print "corrcoef data upper triangular shape: ", K.shape
ten_percent = 0.1
if args.histogram == "all":
# get histogram of upper-triangual array
dbins = 0.01
bins = np.arange(-1, 1+dbins, dbins)
x, bins = np.histogram(K, bins)
# find out threshold value for top 10 percent
back_sum = 0
for idx in range(x.shape[0]-1, -1, -1):
