def estimate_smoothness(self, overwrite=None, imgtype='zstat'):
"""
estimate smoothness of Z maps using FSL's smoothness estimation
"""
log_to_file(
self.dirs.logfile,
sys._getframe().f_code.co_name,
headspace=2)
func_args = inspect.getargvalues(
inspect.currentframe()).locals
log_to_file(
self.dirs.logfile,
stringify_dict(func_args))
if overwrite is None:
overwrite = self.overwrite
output_file = os.path.join(self.dirs.dirs['metadata'],
'smoothness_est.csv')
if os.path.exists(output_file) and not overwrite:
if self.verbose:
print('using existing smoothness file')
smoothness_df = pandas.read_csv(output_file)
return(smoothness_df)
# use nipype's interface to the FSL smoothest command
est = SmoothEstimate()
smoothness = []
for teamID in self.complete_image_sets['unthresh']:
for hyp in range(1, 10):
if hyp not in self.teams[teamID].images['unthresh'][imgtype]:
# fill missing data with nan
print('no zstat present for', teamID, hyp)
smoothness.append([teamID, hyp, numpy.nan,
numpy.nan, numpy.nan])
continue
infile = self.teams[teamID].images['unthresh'][imgtype][hyp]
if not os.path.exists(infile):
print('no image present:', infile)
continue
else:
if self.verbose:
print('estimating smoothness for hyp', hyp)
est.inputs.zstat_file = infile
est.inputs.mask_file = self.dirs.MNI_mask
est.terminal_output = 'file_split'
smoothest_output = est.run()
smoothness.append([teamID, hyp,
smoothest_output.outputs.dlh,
smoothest_output.outputs.volume,
smoothest_output.outputs.resels])
self.teams[teamID].logs['smoothest'] = (
smoothest_output.runtime.stdout,
smoothest_output.runtime.stderr)
smoothness_df = pandas.DataFrame(
smoothness,
columns=['teamID', 'hyp', 'dhl', 'volume', 'resels'])
smoothness_df.to_csv(output_file)
return(smoothness_df)
