t_all=[]
# Load the time series and average over ROI
t_all=load_nii(nifti_path+this_fix[:-4]+'_stc.nii.gz', ROI_coords,TR, normalize='percent', average=True, verbose=True)
for roiNum in range(len(roi_names)):
print 'Analyzing '+ roi_names[roiNum]
ts_roi=[]; ts_roidt=[]; ts_Box=[];
ts_roidv=[]; ts_roidv_dt=[]; ts_roidv_dtBox=[];
# Get each time series (1 x TRs)
ts_roi=t_all[roiNum].data
# Linearly detrend each ROI
ts_roidt=signal.detrend(ts_roi, axis=0)
# Band pass filter the data using boxcar filter
ts_Box=bp_data(ts_roidt, TR, f_ub, f_lb)
# Get the derivative
ts_roidv=np.diff(ts_roi)
# Make it the same size as the original
ts_roidv=np.insert(ts_roidv, 0, 0)
# Detrend the derivative
ts_roidv_dt=signal.detrend(ts_roidv)
#Bandpass the derivative
ts_roidv_dtBox=bp_data(ts_roidv_dt, TR, f_ub, f_lb)
# Plot TS results
#plt.figure(); plt.plot(ts_roi); plt.plot(ts_roidt); plt.plot(ts_Box) ;
#plt.legend(('Original TS', 'Linearly Filtered TS', 'Bandpass filtered'))
# Plot frequencies
for this_fix in sessName[1][runName]:
txt_file=nifti_path+this_fix
1/0
out_file_base=save_path+this_fix[:-4]
# read in par file
data = np.loadtxt(txt_file)
# save the columns of data as separate .1D files
for i in xrange(data.shape[1]):
mt_deriv=[];
# Get derivative
mt_deriv=np.diff(data[:,i])
# Make it the same size as the original
mt_deriv=np.insert(mt_deriv, 0, 0)
# Run bandpass filter
mt_bp=bp_data(data[:,i], TR, f_ub, f_lb)
mt_bp_deriv=bp_data(mt_deriv, TR, f_ub, f_lb)
# Save file
out_file = '{0}{1}_bp.1D'.format(out_file_base, i+1)
np.savetxt(out_file, mt_bp)
# Save file
out_file = '{0}{1}_bp_deriv.1D'.format(out_file_base, i+1)
np.savetxt(out_file, mt_bp_deriv)
