def run(args):
ds = arg2ds(args.data)
verbose(3, 'Concatenation yielded %i samples with %i features' % ds.shape)
# build list of events
events = []
timebased_events = False
if args.event_attrs is not None:
def_attrs = dict([(k, ds.sa[k].value) for k in args.event_attrs])
events = find_events(**def_attrs)
elif args.csv_events is not None:
if args.csv_events == '-':
csv = sys.stdin.read()
import cStringIO
csv = cStringIO.StringIO(csv)
else:
csv = open(args.csv_events, 'rU')
csvt = _load_csv_table(csv)
if not len(csvt):
raise ValueError("no CSV columns found")
if args.onset_column:
csvt['onset'] = csvt[args.onset_column]
nevents = len(csvt[csvt.keys()[0]])
events = []
for ev in xrange(nevents):
events.append(dict([(k, v[ev]) for k, v in csvt.iteritems()]))
elif args.onsets is not None:
if not len(args.onsets):
args.onsets = [i for i in sys.stdin]
# time or sample-based?
if args.time_attr is None:
oconv = int
else:
oconv = float
events = [{'onset': oconv(o)} for o in args.onsets]
elif args.fsl_ev3 is not None:
timebased_events = True
from mvpa2.misc.fsl import FslEV3
events = []
for evsrc in args.fsl_ev3:
events.extend(FslEV3(evsrc).to_events())
if not len(events):
raise ValueError("no events defined")
verbose(2, 'Extracting %i events' % len(events))
if args.event_compression is None:
evmap = None
elif args.event_compression == 'mean':
evmap = FxMapper('features', np.mean, attrfx=merge2first)
elif args.event_compression == 'median':
evmap = FxMapper('features', np.median, attrfx=merge2first)
elif args.event_compression == 'min':
evmap = FxMapper('features', np.min, attrfx=merge2first)
elif args.event_compression == 'max':
evmap = FxMapper('features', np.max, attrfx=merge2first)
# convert to event-related ds
evds = eventrelated_dataset(ds,
events,
time_attr=args.time_attr,
match=args.match_strategy,
event_offset=args.offset,
event_duration=args.duration,
event_mapper=evmap)
# act on all attribute options
evds = process_common_dsattr_opts(evds, args)
# and store
ds2hdf5(evds, args.output, compression=args.hdf5_compression)
return evds
def test_er_nifti_dataset():
# setup data sources
tssrc = os.path.join(pymvpa_dataroot, u'bold.nii.gz')
evsrc = os.path.join(pymvpa_dataroot, 'fslev3.txt')
masrc = os.path.join(pymvpa_dataroot, 'mask.nii.gz')
evs = FslEV3(evsrc).to_events()
# load timeseries
ds_orig = fmri_dataset(tssrc)
# segment into events
ds = eventrelated_dataset(ds_orig, evs, time_attr='time_coords')
# we ask for boxcars of 9s length, and the tr in the file header says 2.5s
# hence we should get round(9.0/2.4) * np.prod((1,20,40) == 3200 features
assert_equal(ds.nfeatures, 3200)
assert_equal(len(ds), len(evs))
# the voxel indices are reflattened after boxcaring , but still 3D
assert_equal(ds.fa.voxel_indices.shape, (ds.nfeatures, 3))
# and they have been broadcasted through all boxcars
assert_array_equal(ds.fa.voxel_indices[:800],
ds.fa.voxel_indices[800:1600])
# each feature got an event offset value
assert_array_equal(ds.fa.event_offsetidx, np.repeat([0, 1, 2, 3], 800))
# check for all event attributes
assert_true('onset' in ds.sa)
assert_true('duration' in ds.sa)
assert_true('features' in ds.sa)
# check samples
origsamples = _load_anyimg(tssrc)[0]
for i, onset in \
enumerate([value2idx(e['onset'], ds_orig.sa.time_coords, 'floor')
for e in evs]):
assert_array_equal(ds.samples[i], origsamples[onset:onset + 4].ravel())
assert_array_equal(ds.sa.time_indices[i], np.arange(onset, onset + 4))
assert_array_equal(ds.sa.time_coords[i],
np.arange(onset, onset + 4) * 2.5)
for evattr in [
a for a in ds.sa
if a.count("event_attrs") and not a.count('event_attrs_event')
]:
assert_array_equal(evs[i]['_'.join(evattr.split('_')[2:])],
ds.sa[evattr].value[i])
# check offset: only the last one exactly matches the tr
assert_array_equal(ds.sa.orig_offset, [1, 1, 0])
# map back into voxel space, should ignore addtional features
nim = map2nifti(ds)
# origsamples has t,x,y,z
if externals.versions['nibabel'] >= '1.2':
vol_shape = nim.shape
else:
vol_shape = nim.get_shape()
assert_equal(vol_shape, origsamples.shape[1:] + (len(ds) * 4, ))
# check shape of a single sample
nim = map2nifti(ds, ds.samples[0])
if externals.versions['nibabel'] >= '1.2':
vol_shape = nim.shape
else:
vol_shape = nim.get_shape()
# pynifti image has [t,]z,y,x
assert_equal(vol_shape, (40, 20, 1, 4))
# and now with masking
ds = fmri_dataset(tssrc, mask=masrc)
ds = eventrelated_dataset(ds, evs, time_attr='time_coords')
nnonzero = len(_load_anyimg(masrc)[0].nonzero()[0])
assert_equal(nnonzero, 530)
# we ask for boxcars of 9s length, and the tr in the file header says 2.5s
# hence we should get round(9.0/2.4) * np.prod((1,20,40) == 3200 features
assert_equal(ds.nfeatures, 4 * 530)
assert_equal(len(ds), len(evs))
# and they have been broadcasted through all boxcars
assert_array_equal(ds.fa.voxel_indices[:nnonzero],
ds.fa.voxel_indices[nnonzero:2 * nnonzero])