def run_qc_temporal(func_path, mask_path,
motion_matrix_path,
subject=None, session=None, scan=None,
verbose=False):
# DVARS
if verbose: print "...dvars"
mean_dvars = mean_dvars_wrapper(func_path, mask_path)
# Mean FD (Jenkinson)
if verbose: print "...mean FD"
mean_fd = mean_fd_wrapper(motion_matrix)
# 3dTout
if verbose: print "...3dTout"
mean_outlier= mean_outlier_timepoints(func_path, mask_path)
# 3dTqual
if verbose: print "...3dTqual"
mean_quality= mean_quality_timepoints(func_path)
# Compile
qc = {
"subject": subject,
"session": session,
"scan": scan,
"dvars": mean_dvars,
"fd": mean_fd,
"outlier": mean_outlier,
"quality": mean_quality
}
return qc
def run_qc_temporal(func_path, mask_path,
motion_matrix_path,
subject=None, session=None, scan=None,
verbose=False, motion_threshold=1.0,
opts=["dvars", "fd", "outlier", "quality"]):
# DVARS
if "dvars" in opts:
if verbose: print "...dvars"
mean_dvars = mean_dvars_wrapper(func_path, mask_path)
else:
mean_dvars = None
# Mean FD (Jenkinson)
if "fd" in opts:
if verbose: print "...mean FD"
(mean_fd, num_fd, percent_fd) = summarize_fd(motion_matrix_path, threshold=motion_threshold)
else:
(mean_fd, num_fd, percent_fd) = (None, None, None)
# 3dTout
if "outlier" in opts:
if verbose: print "...3dTout"
mean_outlier= mean_outlier_timepoints(func_path, mask_path)
else:
mean_outlier= None
# 3dTqual
if "quality" in opts:
if verbose: print "...3dTqual"
mean_quality= mean_quality_timepoints(func_path)
else:
mean_quality= None
# Compile
qc = {
"subject": subject,
"session": session,
"scan": scan,
"dvars": mean_dvars,
"mean_fd": mean_fd,
'num_fd': num_fd,
'perc_fd': percent_fd,
"outlier": mean_outlier,
"quality": mean_quality
}
return qc
