def test_getitem_epochsTFR():
"""Test GetEpochsMixin in the context of EpochsTFR."""
from pandas import DataFrame
# Setup for reading the raw data and select a few trials
raw = read_raw_fif(raw_fname)
events = read_events(event_fname)
n_events = 10
# create fake metadata
rng = np.random.RandomState(42)
rt = rng.uniform(size=(n_events,))
trialtypes = np.array(['face', 'place'])
trial = trialtypes[(rng.uniform(size=(n_events,)) > .5).astype(int)]
meta = DataFrame(dict(RT=rt, Trial=trial))
event_id = dict(a=1, b=2, c=3, d=4)
epochs = Epochs(raw, events[:n_events], event_id=event_id, metadata=meta,
decim=1)
freqs = np.arange(12., 17., 2.) # define frequencies of interest
n_cycles = freqs / 2. # 0.5 second time windows for all frequencies
# Choose time x (full) bandwidth product
time_bandwidth = 4.0 # With 0.5 s time windows, this gives 8 Hz smoothing
power = tfr_multitaper(epochs, freqs=freqs, n_cycles=n_cycles,
use_fft=True, time_bandwidth=time_bandwidth,
return_itc=False, average=False, n_jobs=1)
# Check that power and epochs metadata is the same
assert_metadata_equal(epochs.metadata, power.metadata)
assert_metadata_equal(epochs[::2].metadata, power[::2].metadata)
assert_metadata_equal(epochs['RT < .5'].metadata,
power['RT < .5'].metadata)
# Check that get power is functioning
assert_array_equal(power[3:6].data, power.data[3:6])
assert_array_equal(power[3:6].events, power.events[3:6])
indx_check = (power.metadata['Trial'] == 'face').nonzero()
assert_array_equal(power['Trial == "face"'].events,
power.events[indx_check])
assert_array_equal(power['Trial == "face"'].data,
power.data[indx_check])
# Check that the wrong Key generates a Key Error for Metadata search
with pytest.raises(KeyError):
power['Trialz == "place"']
# Test length function
assert len(power) == n_events
assert len(power[3:6]) == 3
# Test iteration function
for ind, power_ep in enumerate(power):
assert_array_equal(power_ep, power.data[ind])
if ind == 5:
break
# Test that current state is maintained
assert_array_equal(power.next(), power.data[ind + 1])
def test_getitem_epochsTFR():
"""Test GetEpochsMixin in the context of EpochsTFR."""
from pandas import DataFrame
# Setup for reading the raw data and select a few trials
raw = read_raw_fif(raw_fname)
events = read_events(event_fname)
n_events = 10
# create fake metadata
rng = np.random.RandomState(42)
rt = rng.uniform(size=(n_events, ))
trialtypes = np.array(['face', 'place'])
trial = trialtypes[(rng.uniform(size=(n_events, )) > .5).astype(int)]
meta = DataFrame(dict(RT=rt, Trial=trial))
event_id = dict(a=1, b=2, c=3, d=4)
epochs = Epochs(raw,
events[:n_events],
event_id=event_id,
metadata=meta,
decim=1)
freqs = np.arange(12., 17., 2.) # define frequencies of interest
n_cycles = freqs / 2. # 0.5 second time windows for all frequencies
# Choose time x (full) bandwidth product
time_bandwidth = 4.0 # With 0.5 s time windows, this gives 8 Hz smoothing
kwargs = dict(freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
time_bandwidth=time_bandwidth,
return_itc=False,
average=False,
n_jobs=1)
power = tfr_multitaper(epochs, **kwargs)
# Check decim affects sfreq
power_decim = tfr_multitaper(epochs, decim=2, **kwargs)
assert power.info['sfreq'] / 2. == power_decim.info['sfreq']
# Check that power and epochs metadata is the same
assert_metadata_equal(epochs.metadata, power.metadata)
assert_metadata_equal(epochs[::2].metadata, power[::2].metadata)
assert_metadata_equal(epochs['RT < .5'].metadata,
power['RT < .5'].metadata)
# Check that get power is functioning
assert_array_equal(power[3:6].data, power.data[3:6])
assert_array_equal(power[3:6].events, power.events[3:6])
indx_check = (power.metadata['Trial'] == 'face')
try:
indx_check = indx_check.to_numpy()
except Exception:
pass # older Pandas
indx_check = indx_check.nonzero()
assert_array_equal(power['Trial == "face"'].events,
power.events[indx_check])
assert_array_equal(power['Trial == "face"'].data, power.data[indx_check])
# Check that the wrong Key generates a Key Error for Metadata search
with pytest.raises(KeyError):
power['Trialz == "place"']
# Test length function
assert len(power) == n_events
assert len(power[3:6]) == 3
# Test iteration function
for ind, power_ep in enumerate(power):
assert_array_equal(power_ep, power.data[ind])
if ind == 5:
break
# Test that current state is maintained
assert_array_equal(power.next(), power.data[ind + 1])
def test_io():
"""Test TFR IO capacities."""
from pandas import DataFrame
tempdir = _TempDir()
fname = op.join(tempdir, 'test-tfr.h5')
data = np.zeros((3, 2, 3))
times = np.array([.1, .2, .3])
freqs = np.array([.10, .20])
info = mne.create_info(['MEG 001', 'MEG 002', 'MEG 003'], 1000.,
['mag', 'mag', 'mag'])
info['meas_date'] = datetime.datetime(year=2020,
month=2,
day=5,
tzinfo=datetime.timezone.utc)
info._check_consistency()
tfr = AverageTFR(info,
data=data,
times=times,
freqs=freqs,
nave=20,
comment='test',
method='crazy-tfr')
tfr.save(fname)
tfr2 = read_tfrs(fname, condition='test')
assert isinstance(tfr2.info, Info)
assert isinstance(tfr2.info['dev_head_t'], Transform)
assert_array_equal(tfr.data, tfr2.data)
assert_array_equal(tfr.times, tfr2.times)
assert_array_equal(tfr.freqs, tfr2.freqs)
assert_equal(tfr.comment, tfr2.comment)
assert_equal(tfr.nave, tfr2.nave)
pytest.raises(IOError, tfr.save, fname)
tfr.comment = None
# test old meas_date
info['meas_date'] = (1, 2)
tfr.save(fname, overwrite=True)
assert_equal(read_tfrs(fname, condition=0).comment, tfr.comment)
tfr.comment = 'test-A'
tfr2.comment = 'test-B'
fname = op.join(tempdir, 'test2-tfr.h5')
write_tfrs(fname, [tfr, tfr2])
tfr3 = read_tfrs(fname, condition='test-A')
assert_equal(tfr.comment, tfr3.comment)
assert (isinstance(tfr.info, mne.Info))
tfrs = read_tfrs(fname, condition=None)
assert_equal(len(tfrs), 2)
tfr4 = tfrs[1]
assert_equal(tfr2.comment, tfr4.comment)
pytest.raises(ValueError, read_tfrs, fname, condition='nonono')
# Test save of EpochsTFR.
n_events = 5
data = np.zeros((n_events, 3, 2, 3))
# create fake metadata
rng = np.random.RandomState(42)
rt = np.round(rng.uniform(size=(n_events, )), 3)
trialtypes = np.array(['face', 'place'])
trial = trialtypes[(rng.uniform(size=(n_events, )) > .5).astype(int)]
meta = DataFrame(dict(RT=rt, Trial=trial))
# fake events and event_id
events = np.zeros([n_events, 3])
events[:, 0] = np.arange(n_events)
events[:, 2] = np.ones(n_events)
event_id = {'a/b': 1}
tfr = EpochsTFR(info,
data=data,
times=times,
freqs=freqs,
comment='test',
method='crazy-tfr',
events=events,
event_id=event_id,
metadata=meta)
tfr.save(fname, True)
read_tfr = read_tfrs(fname)[0]
assert_array_equal(tfr.data, read_tfr.data)
assert_metadata_equal(tfr.metadata, read_tfr.metadata)
assert_array_equal(tfr.events, read_tfr.events)
assert tfr.event_id == read_tfr.event_id
def test_io():
"""Test TFR IO capacities."""
from pandas import DataFrame
tempdir = _TempDir()
fname = op.join(tempdir, 'test-tfr.h5')
data = np.zeros((3, 2, 3))
times = np.array([.1, .2, .3])
freqs = np.array([.10, .20])
info = mne.create_info(['MEG 001', 'MEG 002', 'MEG 003'], 1000.,
['mag', 'mag', 'mag'])
tfr = AverageTFR(info, data=data, times=times, freqs=freqs,
nave=20, comment='test', method='crazy-tfr')
tfr.save(fname)
tfr2 = read_tfrs(fname, condition='test')
assert_array_equal(tfr.data, tfr2.data)
assert_array_equal(tfr.times, tfr2.times)
assert_array_equal(tfr.freqs, tfr2.freqs)
assert_equal(tfr.comment, tfr2.comment)
assert_equal(tfr.nave, tfr2.nave)
pytest.raises(IOError, tfr.save, fname)
tfr.comment = None
tfr.save(fname, overwrite=True)
assert_equal(read_tfrs(fname, condition=0).comment, tfr.comment)
tfr.comment = 'test-A'
tfr2.comment = 'test-B'
fname = op.join(tempdir, 'test2-tfr.h5')
write_tfrs(fname, [tfr, tfr2])
tfr3 = read_tfrs(fname, condition='test-A')
assert_equal(tfr.comment, tfr3.comment)
assert (isinstance(tfr.info, mne.Info))
tfrs = read_tfrs(fname, condition=None)
assert_equal(len(tfrs), 2)
tfr4 = tfrs[1]
assert_equal(tfr2.comment, tfr4.comment)
pytest.raises(ValueError, read_tfrs, fname, condition='nonono')
# Test save of EpochsTFR.
n_events = 5
data = np.zeros((n_events, 3, 2, 3))
# create fake metadata
rng = np.random.RandomState(42)
rt = np.round(rng.uniform(size=(n_events,)), 3)
trialtypes = np.array(['face', 'place'])
trial = trialtypes[(rng.uniform(size=(n_events,)) > .5).astype(int)]
meta = DataFrame(dict(RT=rt, Trial=trial))
# fake events and event_id
events = np.zeros([n_events, 3])
events[:, 0] = np.arange(n_events)
events[:, 2] = np.ones(n_events)
event_id = dict(a=1)
tfr = EpochsTFR(info, data=data, times=times, freqs=freqs,
comment='test', method='crazy-tfr', events=events,
event_id=event_id, metadata=meta)
tfr.save(fname, True)
read_tfr = read_tfrs(fname)[0]
assert_array_equal(tfr.data, read_tfr.data)
assert_metadata_equal(tfr.metadata, read_tfr.metadata)
assert_array_equal(tfr.events, read_tfr.events)
assert_equal(tfr.event_id, read_tfr.event_id)
