def test_samples_attributes(self):
sa = SampleAttributes(os.path.join(pymvpa_dataroot,
'attributes_literal.txt'),
literallabels=True)
ok_(sa.nrows == 1452, msg='There should be 1452 samples')
# convert to event list, with some custom attr
ev = find_events(**sa)
ok_(len(ev) == 17 * (max(sa.chunks) + 1),
msg='Not all events got detected.')
ok_(ev[0]['targets'] == ev[-1]['targets'] == 'rest',
msg='First and last event are rest condition.')
ok_(ev[-1]['onset'] + ev[-1]['duration'] == sa.nrows,
msg='Something is wrong with the timiing of the events')
def test_samples_attributes(self):
sa = SampleAttributes(os.path.join(pymvpa_dataroot,
'attributes_literal.txt'),
literallabels=True)
ok_(sa.nrows == 1452, msg='There should be 1452 samples')
# convert to event list, with some custom attr
ev = find_events(**sa)
ok_(len(ev) == 17 * (max(sa.chunks) + 1),
msg='Not all events got detected.')
ok_(ev[0]['targets'] == ev[-1]['targets'] == 'rest',
msg='First and last event are rest condition.')
ok_(ev[-1]['onset'] + ev[-1]['duration'] == sa.nrows,
msg='Something is wrong with the timiing of the events')
def test_erdataset():
# 3 chunks, 5 targets, blocks of 5 samples each
nchunks = 3
ntargets = 5
blocklength = 5
nfeatures = 10
targets = np.tile(np.repeat(range(ntargets), blocklength), nchunks)
chunks = np.repeat(np.arange(nchunks), ntargets * blocklength)
samples = np.repeat(
np.arange(nchunks * ntargets * blocklength),
nfeatures).reshape(-1, nfeatures)
ds = dataset_wizard(samples, targets=targets, chunks=chunks)
# check if events are determined properly
evs = find_events(targets=ds.sa.targets, chunks=ds.sa.chunks)
for ev in evs:
assert_equal(ev['duration'], blocklength)
assert_equal(ntargets * nchunks, len(evs))
for t in range(ntargets):
assert_equal(len([ev for ev in evs if ev['targets'] == t]),
nchunks)
# now turn `ds` into an eventreleated dataset
erds = eventrelated_dataset(ds, evs)
# the only unprefixed sample attributes are
assert_equal(sorted([a for a in ds.sa if not a.startswith('event')]),
['chunks', 'targets'])
# samples as expected?
assert_array_equal(erds.samples[0],
np.repeat(np.arange(blocklength), nfeatures))
# that should also be the temporal feature offset
assert_array_equal(erds.samples[0], erds.fa.event_offsetidx)
assert_array_equal(erds.sa.event_onsetidx, np.arange(0,71,5))
# finally we should see two mappers
assert_equal(len(erds.a.mapper), 2)
assert_true(isinstance(erds.a.mapper[0], BoxcarMapper))
assert_true(isinstance(erds.a.mapper[1], FlattenMapper))
#
# now check the same dataset with event descretization
tr = 2.5
ds.sa['time'] = np.arange(nchunks * ntargets * blocklength) * tr
evs = [{'onset': 4.9, 'duration': 6.2}]
# doesn't work without conversion
assert_raises(ValueError, eventrelated_dataset, ds, evs)
erds = eventrelated_dataset(ds, evs, time_attr='time')
assert_equal(len(erds), 1)
assert_array_equal(erds.samples[0], np.repeat(np.arange(1,5), nfeatures))
assert_array_equal(erds.sa.orig_onset, [evs[0]['onset']])
assert_array_equal(erds.sa.orig_duration, [evs[0]['duration']])
assert_array_almost_equal(erds.sa.orig_offset, [2.4])
assert_array_equal(erds.sa.time, [np.arange(2.5, 11, 2.5)])
# now with closest match
erds = eventrelated_dataset(ds, evs, time_attr='time', match='closest')
expected_nsamples = 3
assert_equal(len(erds), 1)
assert_array_equal(erds.samples[0],
np.repeat(np.arange(2,2+expected_nsamples),
nfeatures))
assert_array_equal(erds.sa.orig_onset, [evs[0]['onset']])
assert_array_equal(erds.sa.orig_duration, [evs[0]['duration']])
assert_array_almost_equal(erds.sa.orig_offset, [-0.1])
assert_array_equal(erds.sa.time, [np.arange(5.0, 11, 2.5)])
# now test the way back
results = np.arange(erds.nfeatures)
assert_array_equal(erds.a.mapper.reverse1(results),
results.reshape(expected_nsamples, nfeatures))
# what about multiple results?
nresults = 5
results = dataset_wizard([results] * nresults)
# and let's have an attribute to make it more difficult
results.sa['myattr'] = np.arange(5)
rds = erds.a.mapper.reverse(results)
assert_array_equal(rds,
results.samples.reshape(nresults * expected_nsamples,
nfeatures))
assert_array_equal(rds.sa.myattr, np.repeat(results.sa.myattr,
expected_nsamples))
