def main(datafiles):
for subj_file in datafiles:
subid = gu.get_subid(subj_file, subjstr)
print 'Starting on subject %s'%(subid)
dat, aff = gu.load_nii(subj_file)
nets_dat = dat[:,:,:,net_idx]
nets_diff_array = calculate_diff_map(nets_dat)
nets_diff_outfile = os.path.join(outdir, ''.join(['nets_diff_',subid,'.nii.gz']))
gu.save_nii(nets_diff_outfile, nets_diff_array, aff)
return (float(overlap_nvoxels)/float(roi_nvoxels))
if __name__ == '__main__':
########### Set parameters ##########
datadir = '/home/jagust/rsfmri_ica/GIFT/GICA_d30/BPM/results/PIB_Index_log_scaled/Results_ROIs'
roiglob = os.path.join(datadir,'ic*.nii.gz')
roi_files = glob(roiglob)
template = '/home/jagust/rsfmri_ica/GIFT/GICA_d30/network_volumes/netmasks/ic_masks_4D.nii.gz'
template_mapfile = '/home/jagust/rsfmri_ica/GIFT/GICA_d30/network_volumes/netmasks/template_mapping.txt'
outfile = os.path.join(datadir, 'Network_Labels_GICA30.csv')
####################################
#Load template and 4D roi data
tempdat, _ = utils.load_nii(template)
temp_mapping = utils.load_mapping(template_mapfile)
# Verify the shape of datasets match
roi_labels = {}
for roi in roi_files:
overlap_dict = {}
_,roi_name,_ = utils.split_filename(roi)
roidat, _ = utils.load_nii(roi)
assert tempdat.shape[:-1] == roidat.shape
for j in range(tempdat.shape[3]):
temp_mask = tempdat[:,:,:,j]
pct_overlap = calc_pct_overlap(roidat, temp_mask)
net_name = temp_mapping[str(j+1)].split('.')[0]
overlap_dict[net_name] = pct_overlap
def calc_pct_overlap(roi, target):
roi_nvoxels = roi.sum()
overlap = (roi==target) * roi
overlap_nvoxels = overlap.sum()
return (overlap_nvoxels/roi_nvoxels)
########### Set parameters ##########
datadir = '/home/jagust/rsfmri_ica/cluster_roi/ROIs'
roi4Dfile = os.path.join(datadir,'rm_group_tcorr_cluster_150_4D.nii.gz')
template = '/home/jagust/jelman/templates/Yeo_JNeurophysiol11_MNI152/3mm/Yeo2011_7Networks_4D_LiberalMask.nii.gz'
template_mapfile = '/home/jagust/jelman/templates/Yeo_JNeurophysiol11_MNI152/template_mapping.txt'
outfile = os.path.join(datadir, 'ROI_Labels.csv')
####################################
#Load template and 4D roi data
tempdat, _ = utils.load_nii(template)
temp_mapping = utils.load_mapping(template_mapfile)
roi4Ddat, _ = utils.load_nii(roi4Dfile)
# Verify the shape of datasets match
assert tempdat.shape[:-1] == roi4Ddat.shape[:-1]
roi_labels = {}
for i in range(roi4Ddat.shape[3]):
roidat = roi4Ddat[:,:,:,i]
for j in range(tempdat.shape[3]):
temp_mask = tempdat[:,:,:,j]
pct_overlap = calc_pct_overlap(roidat, temp_mask)
if pct_overlap > .45:
roi_name = 'roi%03d'%(i+1)
label_name = temp_mapping[str(j+1)]
roi_labels[roi_name] = label_name
return (float(overlap_nvoxels) / float(roi_nvoxels))
if __name__ == '__main__':
########### Set parameters ##########
datadir = '/home/jagust/rsfmri_ica/GIFT/GICA_d30/BPM/results/PIB_Index_log_scaled/Results_ROIs'
roiglob = os.path.join(datadir, 'ic*.nii.gz')
roi_files = glob(roiglob)
template = '/home/jagust/rsfmri_ica/GIFT/GICA_d30/network_volumes/netmasks/ic_masks_4D.nii.gz'
template_mapfile = '/home/jagust/rsfmri_ica/GIFT/GICA_d30/network_volumes/netmasks/template_mapping.txt'
outfile = os.path.join(datadir, 'Network_Labels_GICA30.csv')
####################################
#Load template and 4D roi data
tempdat, _ = utils.load_nii(template)
temp_mapping = utils.load_mapping(template_mapfile)
# Verify the shape of datasets match
roi_labels = {}
for roi in roi_files:
overlap_dict = {}
_, roi_name, _ = utils.split_filename(roi)
roidat, _ = utils.load_nii(roi)
assert tempdat.shape[:-1] == roidat.shape
for j in range(tempdat.shape[3]):
temp_mask = tempdat[:, :, :, j]
pct_overlap = calc_pct_overlap(roidat, temp_mask)
net_name = temp_mapping[str(j + 1)].split('.')[0]
overlap_dict[net_name] = pct_overlap
roi_labels[roi_name] = overlap_dict
