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)