def loadrundata(p, s, r, m=None, c=None):
# inputs:
# p: paths list
# s: string representing subject ('LMVPA001')
# r: run ID ('Run1')
from os.path import join as pjoin
from mvpa2.datasets import eventrelated as er
from mvpa2.datasets.mri import fmri_dataset
from mvpa2.datasets.sources import bids as bids
# bfn = pjoin(p[0], 'data', s, 'func', 'extra', s+'_'+r+'_mc.nii.gz')
# motion corrected and coregistered
bfn = pjoin(p[0], 'data', s, 'func', s + '_' + r + '.nii.gz')
if m is not None:
m = pjoin(p[0], 'data', s, 'masks', s + '_' + m + '.nii.gz')
d = fmri_dataset(bfn, chunks=int(r.split('n')[1]), mask=m)
else:
d = fmri_dataset(bfn, chunks=int(r.split('n')[1]))
# This line-- should be different if we're doing GLM, etc.
efn = pjoin(p[0], 'data', s, 'func', s + '_' + r + '.tsv')
fe = bids.load_events(efn)
if c is None:
tmpe = events2dict(fe)
c = tmpe.keys()
if isinstance(c, basestring):
# must be a list/tuple/array for the logic below
c = [c]
for ci in c:
e = adjustevents(fe, ci)
d = er.assign_conditionlabels(d, e, noinfolabel='rest', label_attr=ci)
return d
def loadrundata(p, s, r, m=None, c=None):
# inputs:
# p: paths list
# s: string representing subject ('LMVPA001')
# r: run ID ('Run1')
from os.path import join as pjoin
from mvpa2.datasets import eventrelated as er
from mvpa2.datasets.mri import fmri_dataset
from mvpa2.datasets.sources import bids as bids
# bfn = pjoin(p[0], 'data', s, 'func', 'extra', s+'_'+r+'_mc.nii.gz')
# motion corrected and coregistered
bfn = pjoin(p[0], 'data', s, 'func', s + '_' + r + '.nii.gz')
if m is not None:
m = pjoin(p[0], 'data', s, 'masks', s+'_'+m+'.nii.gz')
d = fmri_dataset(bfn, chunks=int(r.split('n')[1]), mask=m)
else:
d = fmri_dataset(bfn, chunks=int(r.split('n')[1]))
# This line-- should be different if we're doing GLM, etc.
efn = pjoin(p[0], 'data', s, 'func', s + '_' + r + '.tsv')
fe = bids.load_events(efn)
if c is None:
tmpe = events2dict(fe)
c = tmpe.keys()
if isinstance(c, basestring):
# must be a list/tuple/array for the logic below
c = [c]
for ci in c:
e = adjustevents(fe, ci)
d = er.assign_conditionlabels(d, e, noinfolabel='rest', label_attr=ci)
return d
def test_load_events():
evtsv = "onset\tduration"
eq_(load_events(StringIO(evtsv)), [])
ra = load_events(StringIO(evtsv), as_recarr=True)
eq_(len(ra), 0)
eq_(ra.dtype.names, ('onset', 'duration'))
# now with content to do type checks
evtsv = "onset\tduration\ttrial_type\n2\t1.3\tboring\n3.5\t4\texciting"
ra = load_events(StringIO(evtsv))
eq_(ra, [{
'onset': 2.0,
'duration': 1.3,
'trial_type': 'boring'
}, {
'onset': 3.5,
'duration': 4.0,
'trial_type': 'exciting'
}])
def loadevents(p, s):
# if isinstance(c, basestring):
# # must be a list/tuple/array for the logic below
# c = [c]
fds = {}
from mvpa2.datasets.sources import bids
from os.path import join as pjoin
for sub in s.keys():
fds[sub] = [bids.load_events(pjoin(p[0], 'data', sub, 'func', sub + '_' + r + '.tsv')) for r in s[sub]]
return fds
def loadevents(p, s):
# if isinstance(c, basestring):
# # must be a list/tuple/array for the logic below
# c = [c]
fds = {}
from mvpa2.datasets.sources import bids
from os.path import join as pjoin
for sub in s.keys():
fds[sub] = [
bids.load_events(
pjoin(p[0], 'data', sub, 'func', sub + '_' + r + '.tsv'))
for r in s[sub]
]
return fds
