def get_corrvox_gs(data_ts, head_mask, regions):
# remove GS
cf_rm = ConfoundsRm(data_ts[head_mask].mean(0).reshape(-1, 1), data_ts[head_mask].T, intercept=False)
data_ts[head_mask] = cf_rm.transform(data_ts[head_mask].mean(0).reshape(-1, 1), data_ts[head_mask].T).T
# extract time series
ts_regions = ts.get_ts(data_ts, regions)
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions, ts_allvox)
def get_corrvox_gs(data_ts,head_mask, regions):
# remove GS
cf_rm = ConfoundsRm(data_ts[head_mask].mean(0).reshape(-1,1),data_ts[head_mask].T,intercept=False)
data_ts[head_mask] = cf_rm.transform(data_ts[head_mask].mean(0).reshape(-1,1),data_ts[head_mask].T).T
# extract time series
ts_regions = ts.get_ts(data_ts,regions)
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions,ts_allvox)
def grabConnectivityWindowsStats(root_path, part_, windowsize=20):
list_files = listdir(root_path)
means_array = []
stds_array = []
subj_list = []
k = 0
for i in range(len(list_files)):
#try:
if list_files[i].split('.')[-1] == 'mat':
tmp_mat = scipy.io.loadmat(root_path + list_files[i])
tmp_subjid = tmp_mat['subj_id'][0]
tmp_vol = tmp_mat['vol']
else:
tmp_vol = nib.load(root_path + list_files[i]).get_data()
tmp_vol = np.swapaxes(np.swapaxes(tmp_vol, 0, 3), 1, 2)
tmp_subjid = list_files[i].split('_')[1]
print(tmp_subjid)
if tmp_vol.shape[3] > 40:
subj_list.append(tmp_subjid)
ts_ = ts.get_ts(tmp_vol, part_.get_data())
windows_val = clust.getWindows(ts_, windowsize)
print windows_val.shape
tmp_data_mean = windows_val.mean(axis=0)
tmp_data_std = windows_val.std(axis=0)
if k == 0:
means_array = tmp_data_mean[np.newaxis, :]
stds_array = tmp_data_std[np.newaxis, :]
else:
means_array = np.vstack(
(means_array, tmp_data_mean[np.newaxis, :]))
stds_array = np.vstack(
(stds_array, tmp_data_std[np.newaxis, :]))
k += 1
#except:
# print('Exception: ' + root_path + list_files[i])
return pd.DataFrame(means_array,
index=subj_list), pd.DataFrame(stds_array,
index=subj_list)
def grabStability(root_path,part_,nclusters=12,windowsize=20):
list_files = listdir(root_path)
data_array = []
subj_list=[]
k=0
for i in range(len(list_files)):
try:
tmp_mat = scipy.io.loadmat(root_path + list_files[i])
print tmp_mat.keys()
if tmp_mat['vol'].shape[3]>windowsize+20:
subj_list.append(tmp_mat['subj_id'][0])
ts_ = ts.get_ts(tmp_mat['vol'],part_.get_data())
tmp_data2 = clust.getWindowCluster(ts_,nclusters,windowsize).mean(axis=0)
if k==0:
data_array = tmp_data2[np.newaxis,:]
else:
data_array = np.vstack((data_array,tmp_data2[np.newaxis,:]))
k+=1
except:
print('Exception: ' + root_path + list_files[i])
return pd.DataFrame(data_array, index=subj_list)
def grabStability(root_path, part_, nclusters=12, windowsize=20):
list_files = listdir(root_path)
data_array = []
subj_list = []
k = 0
for i in range(len(list_files)):
try:
tmp_mat = scipy.io.loadmat(root_path + list_files[i])
print tmp_mat.keys()
if tmp_mat['vol'].shape[3] > windowsize + 20:
subj_list.append(tmp_mat['subj_id'][0])
ts_ = ts.get_ts(tmp_mat['vol'], part_.get_data())
tmp_data2 = clust.getWindowCluster(ts_, nclusters, windowsize).mean(axis=0)
if k == 0:
data_array = tmp_data2[np.newaxis, :]
else:
data_array = np.vstack((data_array, tmp_data2[np.newaxis, :]))
k += 1
except:
print('Exception: ' + root_path + list_files[i])
return pd.DataFrame(data_array, index=subj_list)
def grabConnectivityWindowsStats(root_path,part_,windowsize=20):
list_files = listdir(root_path)
means_array = []
stds_array = []
subj_list=[]
k=0
for i in range(len(list_files)):
#try:
if list_files[i].split('.')[-1] == 'mat':
tmp_mat = scipy.io.loadmat(root_path + list_files[i])
tmp_subjid = tmp_mat['subj_id'][0]
tmp_vol = tmp_mat['vol']
else:
tmp_vol = nib.load(root_path + list_files[i]).get_data()
tmp_vol = np.swapaxes(np.swapaxes(tmp_vol,0,3),1,2)
tmp_subjid = list_files[i].split('_')[1]
print(tmp_subjid)
if tmp_vol.shape[3]>40:
subj_list.append(tmp_subjid)
ts_ = ts.get_ts(tmp_vol,part_.get_data())
windows_val = clust.getWindows(ts_,windowsize)
print windows_val.shape
tmp_data_mean = windows_val.mean(axis=0)
tmp_data_std = windows_val.std(axis=0)
if k==0:
means_array = tmp_data_mean[np.newaxis,:]
stds_array = tmp_data_std[np.newaxis,:]
else:
means_array = np.vstack((means_array,tmp_data_mean[np.newaxis,:]))
stds_array = np.vstack((stds_array,tmp_data_std[np.newaxis,:]))
k+=1
#except:
# print('Exception: ' + root_path + list_files[i])
return pd.DataFrame(means_array, index=subj_list),pd.DataFrame(stds_array, index=subj_list)
def get_corrvox_std(data_ts,head_mask, regions):
# extract time series std
ts_regions = ts.get_ts(data_ts,regions,metric='std')
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions,ts_allvox)
def get_corrvox_std(data_ts, head_mask, regions):
# extract time series std
ts_regions = ts.get_ts(data_ts, regions, metric='std')
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions, ts_allvox)
