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label_cluster_roi.py
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label_cluster_roi.py
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import os, sys
import numpy as np
import general_utilities as utils
from glob import glob
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
s = pd.Series(roi_labels)
s.to_csv(outfile, header=True,index_label='ROI')