def epoched_spectral_proc(ts_file,sfreq,freq_band,con_method,epoch_window_length):
import numpy as np
from neuropype_ephy.spectral import compute_and_save_spectral_connectivity
data = np.load(ts_file)
print data.shape
print sfreq
print freq_band
if epoch_window_length == None:
conmat_file = compute_and_save_spectral_connectivity(data=data,con_method=con_method,sfreq=sfreq,fmin = freq_band[0],fmax = freq_band[1])
else:
print "Shape before splits:"
print data.shape
print "sfreq:"
print sfreq
nb_splits = data.shape[1] // (epoch_window_length * sfreq)
print "nb_splits:"
print nb_splits
reste = data.shape[1] % int(epoch_window_length * sfreq)
print "reste:"
print reste
if reste != 0:
data = data[:,:-reste]
print "shape after reste:"
print data.shape
print "epoching data with {}s by window, resulting in {} epochs".format(epoch_window_length,nb_splits)
list_epoched_data = np.array_split(data,nb_splits,axis = 1)
for epo in list_epoched_data:
print epo.shape
#print "Shape after splits:"
#print epoched_data.shape
epoched_data = np.array(list_epoched_data)
conmat_file = compute_and_save_spectral_connectivity(data=epoched_data, con_method=con_method, sfreq=sfreq, fmin= freq_band[0], fmax=freq_band[1])
return conmat_file
def _run_interface(self, runtime):
print 'in SpectralConn'
ts_file = self.inputs.ts_file
sfreq = self.inputs.sfreq
freq_band = self.inputs.freq_band
con_method = self.inputs.con_method
epoch_window_length = self.inputs.epoch_window_length
export_to_matlab = self.inputs.export_to_matlab
index = self.inputs.index
if epoch_window_length == traits.Undefined:
print '*** NO epoch_window_length ***'
data = np.load(ts_file)
else:
raw_data = np.load(ts_file)
nb_splits = raw_data.shape[1] // (epoch_window_length * sfreq)
reste = raw_data.shape[1] % int(epoch_window_length * sfreq)
if reste != 0:
raw_data = raw_data[:,:-reste]
print "epoching data with {}s by window, resulting in {} epochs (rest = {})".format(epoch_window_length,nb_splits,reste)
data = np.array(np.array_split(raw_data,nb_splits,axis = 1))
self.conmat_file = compute_and_save_spectral_connectivity(data = data,con_method = con_method,index = index, sfreq=sfreq, fmin= freq_band[0], fmax=freq_band[1],export_to_matlab = export_to_matlab)
return runtime
def spectral_proc_label(ts_file,sfreq,freq_band,con_method,label,mode):
import numpy as np
#import os
from neuropype_ephy.spectral import compute_and_save_spectral_connectivity
data = np.load(ts_file)
conmat_file = compute_and_save_spectral_connectivity(data = data,con_method = con_method,sfreq=sfreq, fmin= freq_band[0], fmax=freq_band[1],index = label,mode = mode)
return conmat_file
