def _bids2nipypeinfo(in_file,
events_file,
regressors_file,
regressors_names=None,
motion_columns=None,
decimals=3,
amplitude=1.0,
removeTR=4,
lastTR=496,
thr=0.5):
from pathlib import Path
import numpy as np
import pandas as pd
from scrubFunc import scrub
from nipype.interfaces.base.support import Bunch
# Process the events file
events = pd.read_csv(events_file)
bunch_fields = ['onsets', 'durations', 'amplitudes']
if not motion_columns:
from itertools import product
motion_columns = [
'_'.join(v) for v in product(('trans', 'rot'), 'xyz')
]
out_motion = Path('motion.par').resolve()
#regress_data = pd.read_csv(regressors_file, sep=r'\s+')
regress_data = scrub(regressors_file,
thr) # grab also per which will be saved as file
np.savetxt(
out_motion,
regress_data[motion_columns].values[removeTR:lastTR + removeTR, ],
'%g')
if regressors_names is None:
regressors_names = sorted(
set(regress_data.columns) - set(motion_columns))
if regressors_names:
bunch_fields += ['regressor_names']
bunch_fields += ['regressors']
runinfo = Bunch(scans=in_file,
conditions=list(set(events.trial_type.values)),
**{k: []
for k in bunch_fields})
for condition in runinfo.conditions:
event = events[events.trial_type.str.match(condition)]
runinfo.onsets.append(
np.round(event.onset.values - removeTR, 3).tolist()
) # added -removeTR to align to the onsets after removing X number of TRs from the scan
runinfo.durations.append(np.round(event.duration.values, 3).tolist())
if 'amplitudes' in events.columns:
runinfo.amplitudes.append(
np.round(event.amplitudes.values, 3).tolist())
else:
runinfo.amplitudes.append([amplitude] * len(event))
if 'regressor_names' in bunch_fields:
runinfo.regressor_names = regressors_names
runinfo.regressors = regress_data[regressors_names].fillna(0.0).values[
removeTR:lastTR +
removeTR, ].T.tolist() # adding removeTR to cut the first rows
return runinfo, str(out_motion)
def saveScrub(regressors_file, thr):
from pathlib import Path
import numpy as np
import pandas as pd
from scrubFunc import scrub
# this function will call scrub and save a file with precentage of scrubbed framewise_displacement
perFile = Path('percentScrub.txt').resolve()
regress_data = pd.read_csv(regressors_file, sep=r'\s+')
regress_data = scrub(regressors_file, thr) # grab also per which will be saved as file
x = regress_data.scrub
per = np.array([sum(x)/len(x)])
np.savetxt(perFile, per, '%g')
return str(perFile)