def test_tfr_multitaper():
"""Test tfr_multitaper"""
sfreq = 200.0
ch_names = ['SIM0001', 'SIM0002', 'SIM0003']
ch_types = ['grad', 'grad', 'grad']
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
n_times = int(sfreq) # Second long epochs
n_epochs = 3
seed = 42
rng = np.random.RandomState(seed)
noise = 0.1 * rng.randn(n_epochs, len(ch_names), n_times)
t = np.arange(n_times, dtype=np.float) / sfreq
signal = np.sin(np.pi * 2. * 50. * t) # 50 Hz sinusoid signal
signal[np.logical_or(t < 0.45, t > 0.55)] = 0. # Hard windowing
on_time = np.logical_and(t >= 0.45, t <= 0.55)
signal[on_time] *= np.hanning(on_time.sum()) # Ramping
dat = noise + signal
reject = dict(grad=4000.)
events = np.empty((n_epochs, 3), int)
first_event_sample = 100
event_id = dict(sin50hz=1)
for k in range(n_epochs):
events[k, :] = first_event_sample + k * n_times, 0, event_id['sin50hz']
epochs = EpochsArray(data=dat, info=info, events=events, event_id=event_id,
reject=reject)
freqs = np.arange(5, 100, 3, dtype=np.float)
power, itc = tfr_multitaper(epochs, freqs=freqs, n_cycles=freqs / 2.,
time_bandwidth=4.0)
picks = np.arange(len(ch_names))
power_picks, itc_picks = tfr_multitaper(epochs, freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0, picks=picks)
power_evoked = tfr_multitaper(epochs.average(), freqs=freqs,
n_cycles=freqs / 2., time_bandwidth=4.0,
return_itc=False)
# test picks argument
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(itc.data, itc_picks.data)
# one is squared magnitude of the average (evoked) and
# the other is average of the squared magnitudes (epochs PSD)
# so values shouldn't match, but shapes should
assert_array_equal(power.data.shape, power_evoked.data.shape)
assert_raises(AssertionError, assert_array_almost_equal,
power.data, power_evoked.data)
tmax = t[np.argmax(itc.data[0, freqs == 50, :])]
fmax = freqs[np.argmax(power.data[1, :, t == 0.5])]
assert_true(tmax > 0.3 and tmax < 0.7)
assert_false(np.any(itc.data < 0.))
assert_true(fmax > 40 and fmax < 60)
def test_tfr_multitaper():
"""Test tfr_multitaper"""
sfreq = 200.0
ch_names = ['SIM0001', 'SIM0002', 'SIM0003']
ch_types = ['grad', 'grad', 'grad']
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
n_times = int(sfreq) # Second long epochs
n_epochs = 3
seed = 42
rng = np.random.RandomState(seed)
noise = 0.1 * rng.randn(n_epochs, len(ch_names), n_times)
t = np.arange(n_times, dtype=np.float) / sfreq
signal = np.sin(np.pi * 2. * 50. * t) # 50 Hz sinusoid signal
signal[np.logical_or(t < 0.45, t > 0.55)] = 0. # Hard windowing
on_time = np.logical_and(t >= 0.45, t <= 0.55)
signal[on_time] *= np.hanning(on_time.sum()) # Ramping
dat = noise + signal
reject = dict(grad=4000.)
events = np.empty((n_epochs, 3), int)
first_event_sample = 100
event_id = dict(sin50hz=1)
for k in range(n_epochs):
events[k, :] = first_event_sample + k * n_times, 0, event_id['sin50hz']
epochs = EpochsArray(data=dat,
info=info,
events=events,
event_id=event_id,
reject=reject)
freqs = np.arange(5, 100, 3, dtype=np.float)
power, itc = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0)
power_evoked = tfr_multitaper(epochs.average(),
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
return_itc=False)
# one is squared magnitude of the average (evoked) and
# the other is average of the squared magnitudes (epochs PSD)
# so values shouldn't match, but shapes should
assert_array_equal(power.data.shape, power_evoked.data.shape)
assert_raises(AssertionError, assert_array_almost_equal, power.data,
power_evoked.data)
tmax = t[np.argmax(itc.data[0, freqs == 50, :])]
fmax = freqs[np.argmax(power.data[1, :, t == 0.5])]
assert_true(tmax > 0.3 and tmax < 0.7)
assert_false(np.any(itc.data < 0.))
assert_true(fmax > 40 and fmax < 60)
def test_add_noise():
"""Test noise addition."""
if check_version('numpy', '1.17'):
rng = np.random.default_rng(0)
else:
rng = np.random.RandomState(0)
raw = read_raw_fif(raw_fname)
raw.del_proj()
picks = pick_types(raw.info, meg=True, eeg=True, exclude=())
cov = compute_raw_covariance(raw, picks=picks)
with pytest.raises(RuntimeError, match='to be loaded'):
add_noise(raw, cov)
raw.crop(0, 1).load_data()
with pytest.raises(TypeError, match='Raw, Epochs, or Evoked'):
add_noise(0., cov)
with pytest.raises(TypeError, match='Covariance'):
add_noise(raw, 0.)
# test a no-op (data preserved)
orig_data = raw[:][0]
zero_cov = cov.copy()
zero_cov['data'].fill(0)
add_noise(raw, zero_cov)
new_data = raw[:][0]
assert_allclose(orig_data, new_data, atol=1e-30)
# set to zero to make comparisons easier
raw._data[:] = 0.
epochs = EpochsArray(np.zeros((1, len(raw.ch_names), 100)),
raw.info.copy())
epochs.info['bads'] = []
evoked = epochs.average(picks=np.arange(len(raw.ch_names)))
for inst in (raw, epochs, evoked):
with catch_logging() as log:
add_noise(inst, cov, random_state=rng, verbose=True)
log = log.getvalue()
want = ('to {0}/{1} channels ({0}'
.format(len(cov['names']), len(raw.ch_names)))
assert want in log
if inst is evoked:
inst = EpochsArray(inst.data[np.newaxis], inst.info)
if inst is raw:
cov_new = compute_raw_covariance(inst, picks=picks)
else:
cov_new = compute_covariance(inst)
assert cov['names'] == cov_new['names']
r = np.corrcoef(cov['data'].ravel(), cov_new['data'].ravel())[0, 1]
assert r > 0.99
def test_add_noise():
"""Test noise addition."""
rng = np.random.RandomState(0)
data_path = testing.data_path()
raw = read_raw_fif(data_path + '/MEG/sample/sample_audvis_trunc_raw.fif')
raw.del_proj()
picks = pick_types(raw.info, eeg=True, exclude=())
cov = compute_raw_covariance(raw, picks=picks)
with pytest.raises(RuntimeError, match='to be loaded'):
add_noise(raw, cov)
raw.crop(0, 1).load_data()
with pytest.raises(TypeError, match='Raw, Epochs, or Evoked'):
add_noise(0., cov)
with pytest.raises(TypeError, match='Covariance'):
add_noise(raw, 0.)
# test a no-op (data preserved)
orig_data = raw[:][0]
zero_cov = cov.copy()
zero_cov['data'].fill(0)
add_noise(raw, zero_cov)
new_data = raw[:][0]
assert_allclose(orig_data, new_data, atol=1e-30)
# set to zero to make comparisons easier
raw._data[:] = 0.
epochs = EpochsArray(np.zeros((1, len(raw.ch_names), 100)),
raw.info.copy())
epochs.info['bads'] = []
evoked = epochs.average(picks=np.arange(len(raw.ch_names)))
for inst in (raw, epochs, evoked):
with catch_logging() as log:
add_noise(inst, cov, random_state=rng, verbose=True)
log = log.getvalue()
want = ('to {0}/{1} channels ({0}'
.format(len(cov['names']), len(raw.ch_names)))
assert want in log
if inst is evoked:
inst = EpochsArray(inst.data[np.newaxis], inst.info)
if inst is raw:
cov_new = compute_raw_covariance(inst, picks=picks,
verbose='error') # samples
else:
cov_new = compute_covariance(inst, verbose='error') # avg ref
assert cov['names'] == cov_new['names']
r = np.corrcoef(cov['data'].ravel(), cov_new['data'].ravel())[0, 1]
assert r > 0.99
def test_decim():
"""Test evoked decimation."""
rng = np.random.RandomState(0)
n_epochs, n_channels, n_times = 5, 10, 20
dec_1, dec_2 = 2, 3
decim = dec_1 * dec_2
sfreq = 1000.
sfreq_new = sfreq / decim
data = rng.randn(n_epochs, n_channels, n_times)
events = np.array([np.arange(n_epochs), [0] * n_epochs, [1] * n_epochs]).T
info = create_info(n_channels, sfreq, 'eeg')
info['lowpass'] = sfreq_new / float(decim)
epochs = EpochsArray(data, info, events)
data_epochs = epochs.copy().decimate(decim).get_data()
data_epochs_2 = epochs.copy().decimate(decim, offset=1).get_data()
data_epochs_3 = epochs.decimate(dec_1).decimate(dec_2).get_data()
assert_array_equal(data_epochs, data[:, :, ::decim])
assert_array_equal(data_epochs_2, data[:, :, 1::decim])
assert_array_equal(data_epochs, data_epochs_3)
# Now let's do it with some real data
raw = read_raw_fif(raw_fname, add_eeg_ref=False)
events = read_events(event_name)
sfreq_new = raw.info['sfreq'] / decim
raw.info['lowpass'] = sfreq_new / 4. # suppress aliasing warnings
picks = pick_types(raw.info, meg=True, eeg=True, exclude=())
epochs = Epochs(raw, events, 1, -0.2, 0.5, picks=picks, preload=True,
add_eeg_ref=False)
for offset in (0, 1):
ev_ep_decim = epochs.copy().decimate(decim, offset).average()
ev_decim = epochs.average().decimate(decim, offset)
expected_times = epochs.times[offset::decim]
assert_allclose(ev_decim.times, expected_times)
assert_allclose(ev_ep_decim.times, expected_times)
expected_data = epochs.get_data()[:, :, offset::decim].mean(axis=0)
assert_allclose(ev_decim.data, expected_data)
assert_allclose(ev_ep_decim.data, expected_data)
assert_equal(ev_decim.info['sfreq'], sfreq_new)
assert_array_equal(ev_decim.times, expected_times)
def test_tfr_multitaper():
"""Test tfr_multitaper."""
sfreq = 200.0
ch_names = ['SIM0001', 'SIM0002']
ch_types = ['grad', 'grad']
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
n_times = int(sfreq) # Second long epochs
n_epochs = 3
seed = 42
rng = np.random.RandomState(seed)
noise = 0.1 * rng.randn(n_epochs, len(ch_names), n_times)
t = np.arange(n_times, dtype=np.float64) / sfreq
signal = np.sin(np.pi * 2. * 50. * t) # 50 Hz sinusoid signal
signal[np.logical_or(t < 0.45, t > 0.55)] = 0. # Hard windowing
on_time = np.logical_and(t >= 0.45, t <= 0.55)
signal[on_time] *= np.hanning(on_time.sum()) # Ramping
dat = noise + signal
reject = dict(grad=4000.)
events = np.empty((n_epochs, 3), int)
first_event_sample = 100
event_id = dict(sin50hz=1)
for k in range(n_epochs):
events[k, :] = first_event_sample + k * n_times, 0, event_id['sin50hz']
epochs = EpochsArray(data=dat,
info=info,
events=events,
event_id=event_id,
reject=reject)
freqs = np.arange(35, 70, 5, dtype=np.float64)
power, itc = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0)
power2, itc2 = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
decim=slice(0, 2))
picks = np.arange(len(ch_names))
power_picks, itc_picks = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
picks=picks)
power_epochs = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
return_itc=False,
average=False)
power_averaged = power_epochs.average()
power_evoked = tfr_multitaper(epochs.average(),
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
return_itc=False,
average=False).average()
print(power_evoked) # test repr for EpochsTFR
# Test channel picking
power_epochs_picked = power_epochs.copy().drop_channels(['SIM0002'])
assert_equal(power_epochs_picked.data.shape, (3, 1, 7, 200))
assert_equal(power_epochs_picked.ch_names, ['SIM0001'])
pytest.raises(ValueError,
tfr_multitaper,
epochs,
freqs=freqs,
n_cycles=freqs / 2.,
return_itc=True,
average=False)
# test picks argument
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(power.data, power_averaged.data)
assert_array_almost_equal(power.times, power_epochs.times)
assert_array_almost_equal(power.times, power_averaged.times)
assert_equal(power.nave, power_averaged.nave)
assert_equal(power_epochs.data.shape, (3, 2, 7, 200))
assert_array_almost_equal(itc.data, itc_picks.data)
# one is squared magnitude of the average (evoked) and
# the other is average of the squared magnitudes (epochs PSD)
# so values shouldn't match, but shapes should
assert_array_equal(power.data.shape, power_evoked.data.shape)
pytest.raises(AssertionError, assert_array_almost_equal, power.data,
power_evoked.data)
tmax = t[np.argmax(itc.data[0, freqs == 50, :])]
fmax = freqs[np.argmax(power.data[1, :, t == 0.5])]
assert (tmax > 0.3 and tmax < 0.7)
assert not np.any(itc.data < 0.)
assert (fmax > 40 and fmax < 60)
assert (power2.data.shape == (len(picks), len(freqs), 2))
assert (power2.data.shape == itc2.data.shape)
# Test decim parameter checks and compatibility between wavelets length
# and instance length in the time dimension.
pytest.raises(TypeError,
tfr_multitaper,
epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
decim=(1, ))
pytest.raises(ValueError,
tfr_multitaper,
epochs,
freqs=freqs,
n_cycles=1000,
time_bandwidth=4.0)
# Test invalid frequency arguments
with pytest.raises(ValueError, match=" 'freqs' must be greater than 0"):
tfr_multitaper(epochs, freqs=np.arange(0, 3), n_cycles=7)
with pytest.raises(ValueError, match=" 'freqs' must be greater than 0"):
tfr_multitaper(epochs, freqs=np.arange(-4, -1), n_cycles=7)
def test_tfr_multitaper():
"""Test tfr_multitaper."""
sfreq = 200.0
ch_names = ['SIM0001', 'SIM0002']
ch_types = ['grad', 'grad']
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
n_times = int(sfreq) # Second long epochs
n_epochs = 3
seed = 42
rng = np.random.RandomState(seed)
noise = 0.1 * rng.randn(n_epochs, len(ch_names), n_times)
t = np.arange(n_times, dtype=np.float) / sfreq
signal = np.sin(np.pi * 2. * 50. * t) # 50 Hz sinusoid signal
signal[np.logical_or(t < 0.45, t > 0.55)] = 0. # Hard windowing
on_time = np.logical_and(t >= 0.45, t <= 0.55)
signal[on_time] *= np.hanning(on_time.sum()) # Ramping
dat = noise + signal
reject = dict(grad=4000.)
events = np.empty((n_epochs, 3), int)
first_event_sample = 100
event_id = dict(sin50hz=1)
for k in range(n_epochs):
events[k, :] = first_event_sample + k * n_times, 0, event_id['sin50hz']
epochs = EpochsArray(data=dat, info=info, events=events, event_id=event_id,
reject=reject)
freqs = np.arange(35, 70, 5, dtype=np.float)
power, itc = tfr_multitaper(epochs, freqs=freqs, n_cycles=freqs / 2.,
time_bandwidth=4.0)
power2, itc2 = tfr_multitaper(epochs, freqs=freqs, n_cycles=freqs / 2.,
time_bandwidth=4.0, decim=slice(0, 2))
picks = np.arange(len(ch_names))
power_picks, itc_picks = tfr_multitaper(epochs, freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0, picks=picks)
power_epochs = tfr_multitaper(epochs, freqs=freqs,
n_cycles=freqs / 2., time_bandwidth=4.0,
return_itc=False, average=False)
power_averaged = power_epochs.average()
power_evoked = tfr_multitaper(epochs.average(), freqs=freqs,
n_cycles=freqs / 2., time_bandwidth=4.0,
return_itc=False, average=False).average()
print(power_evoked) # test repr for EpochsTFR
# Test channel picking
power_epochs_picked = power_epochs.copy().drop_channels(['SIM0002'])
assert_equal(power_epochs_picked.data.shape, (3, 1, 7, 200))
assert_equal(power_epochs_picked.ch_names, ['SIM0001'])
pytest.raises(ValueError, tfr_multitaper, epochs,
freqs=freqs, n_cycles=freqs / 2.,
return_itc=True, average=False)
# test picks argument
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(power.data, power_averaged.data)
assert_array_almost_equal(power.times, power_epochs.times)
assert_array_almost_equal(power.times, power_averaged.times)
assert_equal(power.nave, power_averaged.nave)
assert_equal(power_epochs.data.shape, (3, 2, 7, 200))
assert_array_almost_equal(itc.data, itc_picks.data)
# one is squared magnitude of the average (evoked) and
# the other is average of the squared magnitudes (epochs PSD)
# so values shouldn't match, but shapes should
assert_array_equal(power.data.shape, power_evoked.data.shape)
pytest.raises(AssertionError, assert_array_almost_equal,
power.data, power_evoked.data)
tmax = t[np.argmax(itc.data[0, freqs == 50, :])]
fmax = freqs[np.argmax(power.data[1, :, t == 0.5])]
assert (tmax > 0.3 and tmax < 0.7)
assert not np.any(itc.data < 0.)
assert (fmax > 40 and fmax < 60)
assert (power2.data.shape == (len(picks), len(freqs), 2))
assert (power2.data.shape == itc2.data.shape)
# Test decim parameter checks and compatibility between wavelets length
# and instance length in the time dimension.
pytest.raises(TypeError, tfr_multitaper, epochs, freqs=freqs,
n_cycles=freqs / 2., time_bandwidth=4.0, decim=(1,))
pytest.raises(ValueError, tfr_multitaper, epochs, freqs=freqs,
n_cycles=1000, time_bandwidth=4.0)
data = noise + signal
reject = dict(grad=4000)
events = np.empty((n_epochs, 3), dtype=int)
first_event_sample = 100
event_id = dict(sin50hz=1)
for k in range(n_epochs):
events[k, :] = first_event_sample + k * n_times, 0, event_id['sin50hz']
epochs = EpochsArray(data=data,
info=info,
events=events,
event_id=event_id,
reject=reject)
epochs.average().plot()
# %%
# Calculate a time-frequency representation (TFR)
# -----------------------------------------------
#
# Below we'll demonstrate the output of several TFR functions in MNE:
#
# * :func:`mne.time_frequency.tfr_multitaper`
# * :func:`mne.time_frequency.tfr_stockwell`
# * :func:`mne.time_frequency.tfr_morlet`
#
# Multitaper transform
# ====================
# First we'll use the multitaper method for calculating the TFR.
# This creates several orthogonal tapering windows in the TFR estimation,
signal[np.logical_or(t < 0.45, t > 0.55)] = 0. # Hard windowing
on_time = np.logical_and(t >= 0.45, t <= 0.55)
signal[on_time] *= np.hanning(on_time.sum()) # Ramping
data = noise + signal
reject = dict(grad=4000)
events = np.empty((n_epochs, 3), dtype=int)
first_event_sample = 100
event_id = dict(sin50hz=1)
for k in range(n_epochs):
events[k, :] = first_event_sample + k * n_times, 0, event_id['sin50hz']
epochs = EpochsArray(data=data, info=info, events=events, event_id=event_id,
reject=reject)
epochs.average().plot()
###############################################################################
# Calculate a time-frequency representation (TFR)
# -----------------------------------------------
#
# Below we'll demonstrate the output of several TFR functions in MNE:
#
# * :func:`mne.time_frequency.tfr_multitaper`
# * :func:`mne.time_frequency.tfr_stockwell`
# * :func:`mne.time_frequency.tfr_morlet`
#
# Multitaper transform
# ====================
# First we'll use the multitaper method for calculating the TFR.
# This creates several orthogonal tapering windows in the TFR estimation,