########### 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
df = pd.DataFrame(roi_labels)
df.to_csv(outfile, header=True,index_label='Network')
########### 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
df = pd.DataFrame(roi_labels)
df.to_csv(outfile, header=True, index_label='Network')
def get_seedname(seedfile):
_, nme, _ = utils.split_filename(seedfile)
return nme
custom_palette = sns.color_palette(["#377eb8","#4daf4a"])
#ex. colors: "#e41a1c"=red, "#377eb8"=blue, "#4daf4a"=green
plotcols = statlist
col_labels = ['1', '2', '3', '4', '5']
plt_outfile_pattern = '/home/jagust/DST/FSL/results/Details/%s.svg'
#Specify info to extract GM values
gm_pattern = '/home/jagust/DST/FSL/functional/%s/run02/Detail_ST.feat/reg/highresGMs2standard.nii.gz'
####################################################################
#Load subject list and group info file
with open(sublist_file,'r+') as f:
sublist = f.read().splitlines()
groupinfo = pd.read_csv(groupinfo_file, sep=',')
#Set output file names
pth, mask_name, ext = gu.split_filename(mask)
outfile = outfile_pattern%(mask_name)
if plot_fig:
plt_outfile = plt_outfile_pattern%(mask_name)
#Extract GM values
gmdict = {}
for subj in sublist:
print subj
gm_infile = gm_pattern%(subj)
subjstat = fslstats(gm_infile, mask)
gmdict[subj] = subjstat
gmdict = {'GM':gmdict}
gmdf = pd.DataFrame.from_dict(gmdict, orient='columns')
#Extract stats from functional data
