def transform(self, epochs):
from mne import EpochsArray
from mne.time_frequency import single_trial_power
sfreq = epochs.info['sfreq']
# Time Frequency decomposition
tfr = single_trial_power(epochs._data, sfreq=sfreq, **self.tfr_kwargs)
# Consider frequencies as if it was different time points
n_trial, n_chan, n_freq, n_time = tfr.shape
tfr = np.reshape(tfr, [n_trial, n_chan, n_freq * n_time])
# Make pseudo epochs
sfreq = epochs.info['sfreq']
decim = self.tfr_kwargs.get('decim', None)
if isinstance(decim, slice):
decim = decim.step
if decim is not None and decim > 1:
sfreq /= decim
info = epochs.info.copy()
info['sfreq'] = sfreq
self._tfr_epochs = EpochsArray(data=tfr,
info=info,
events=epochs.events)
def transform(self, epochs):
from mne import EpochsArray
from mne.time_frequency import single_trial_power
sfreq = epochs.info['sfreq']
# Time Frequency decomposition
tfr = single_trial_power(epochs._data, sfreq=sfreq,
**self.tfr_kwargs)
# Consider frequencies as if it was different time points
n_trial, n_chan, n_freq, n_time = tfr.shape
tfr = np.reshape(tfr, [n_trial, n_chan, n_freq * n_time])
# Make pseudo epochs
sfreq = epochs.info['sfreq']
decim = self.tfr_kwargs.get('decim', None)
if isinstance(decim, slice):
decim = decim.step
if decim is not None and decim > 1:
sfreq /= decim
info = epochs.info.copy()
info['sfreq'] = sfreq
self._tfr_epochs = EpochsArray(data=tfr, info=info,
events=epochs.events)
def test_time_frequency():
"""Test time frequency transform (PSD and phase lock)
"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = io.Raw(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False,
stim=False, include=include, exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
data = epochs.get_data()
times = epochs.times
frequencies = np.arange(6, 20, 5) # define frequencies of interest
Fs = raw.info['sfreq'] # sampling in Hz
n_cycles = frequencies / float(4)
power, phase_lock = induced_power(data, Fs=Fs, frequencies=frequencies,
n_cycles=n_cycles, use_fft=True)
assert_true(power.shape == (len(picks), len(frequencies), len(times)))
assert_true(power.shape == phase_lock.shape)
assert_true(np.sum(phase_lock >= 1) == 0)
assert_true(np.sum(phase_lock <= 0) == 0)
power, phase_lock = induced_power(data, Fs=Fs, frequencies=frequencies,
n_cycles=2, use_fft=False)
assert_true(power.shape == (len(picks), len(frequencies), len(times)))
assert_true(power.shape == phase_lock.shape)
assert_true(np.sum(phase_lock >= 1) == 0)
assert_true(np.sum(phase_lock <= 0) == 0)
tfr = cwt_morlet(data[0], Fs, frequencies, use_fft=True, n_cycles=2)
assert_true(tfr.shape == (len(picks), len(frequencies), len(times)))
single_power = single_trial_power(data, Fs, frequencies, use_fft=False,
n_cycles=2)
assert_array_almost_equal(np.mean(single_power), power)
def test_time_frequency():
"""Test time frequency transform (PSD and phase lock)
"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = fiff.Raw(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = fiff.pick_types(raw.info, meg='grad', eeg=False,
stim=False, include=include, exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
data = epochs.get_data()
times = epochs.times
frequencies = np.arange(6, 20, 5) # define frequencies of interest
Fs = raw.info['sfreq'] # sampling in Hz
n_cycles = frequencies / float(4)
power, phase_lock = induced_power(data, Fs=Fs, frequencies=frequencies,
n_cycles=n_cycles, use_fft=True)
assert_true(power.shape == (len(picks), len(frequencies), len(times)))
assert_true(power.shape == phase_lock.shape)
assert_true(np.sum(phase_lock >= 1) == 0)
assert_true(np.sum(phase_lock <= 0) == 0)
power, phase_lock = induced_power(data, Fs=Fs, frequencies=frequencies,
n_cycles=2, use_fft=False)
assert_true(power.shape == (len(picks), len(frequencies), len(times)))
assert_true(power.shape == phase_lock.shape)
assert_true(np.sum(phase_lock >= 1) == 0)
assert_true(np.sum(phase_lock <= 0) == 0)
tfr = cwt_morlet(data[0], Fs, frequencies, use_fft=True, n_cycles=2)
assert_true(tfr.shape == (len(picks), len(frequencies), len(times)))
single_power = single_trial_power(data, Fs, frequencies, use_fft=False,
n_cycles=2)
assert_array_almost_equal(np.mean(single_power), power)
def transform(self, epochs):
from mne import EpochsArray
from mne.time_frequency import single_trial_power
# Time Frequency decomposition
tfr = single_trial_power(epochs._data, sfreq=epochs.info['sfreq'],
**self.tfr_kwargs)
# Consider frequencies as if it was different time points
n_trial, n_chan, n_freq, n_time = tfr.shape
tfr = np.reshape(tfr, [n_trial, n_chan, n_freq * n_time])
# Make pseudo epochs
sfreq = epochs.info['sfreq']
if 'decim' in self.tfr_kwargs.keys():
sfreq /= self.tfr_kwargs['decim']
info = epochs.info.copy()
info['sfreq'] = sfreq
self._tfr_epochs = EpochsArray(data=tfr, info=info,
events=epochs.events)
# Time vector
times = 1e3 * epochs.times # change unit to ms
# Factor to downs-sample the temporal dimension of the PSD computed by
# single_trial_power.
decim = 2
frequencies = np.arange(7, 30, 3) # define frequencies of interest
sfreq = raw.info['sfreq'] # sampling in Hz
n_cycles = frequencies / frequencies[0]
baseline_mask = times[::decim] < 0
# now create TFR representations for all conditions
epochs_power = []
for condition in [epochs[k].get_data()[:, 97:98, :] for k in event_id]:
this_power = single_trial_power(condition, sfreq=sfreq,
frequencies=frequencies, n_cycles=n_cycles,
decim=decim)
this_power = this_power[:, 0, :, :] # we only have one channel.
# Compute ratio with baseline power (be sure to correct time vector with
# decimation factor)
epochs_baseline = np.mean(this_power[:, :, baseline_mask], axis=2)
this_power /= epochs_baseline[..., np.newaxis]
epochs_power.append(this_power)
###############################################################################
# Setup repeated measures ANOVA
n_conditions = len(epochs.event_id)
n_replications = epochs.events.shape[0] / n_conditions
# we will tell the ANOVA how to interpret the data matrix in terms of
# factors. This done via the factor levels argument which is a list
ch_name = raw.info['ch_names'][picks[0]]
times = 1e3 * epochs_condition_1.times # change unit to ms
# Factor to downsample the temporal dimension of the PSD computed by
# single_trial_power. Decimation occurs after frequency decomposition and can
# be used to reduce memory usage (and possibly comptuational time of downstream
# operations such as nonparametric statistics) if you don't need high
# spectrotemporal resolution.
decim = 2
frequencies = np.arange(7, 30, 3) # define frequencies of interest
sfreq = raw.info['sfreq'] # sampling in Hz
n_cycles = 1.5
epochs_power_1 = single_trial_power(data_condition_1,
sfreq=sfreq,
frequencies=frequencies,
n_cycles=n_cycles,
decim=decim)
epochs_power_2 = single_trial_power(data_condition_2,
sfreq=sfreq,
frequencies=frequencies,
n_cycles=n_cycles,
decim=decim)
epochs_power_1 = epochs_power_1[:, 0, :, :] # only 1 channel to get 3D matrix
epochs_power_2 = epochs_power_2[:, 0, :, :] # only 1 channel to get 3D matrix
# Compute ratio with baseline power (be sure to correct time vector with
# decimation factor)
baseline_mask = times[::decim] < 0
def test_time_frequency():
"""Test time frequency transform (PSD and phase lock)
"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = io.Raw(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info,
meg='grad',
eeg=False,
stim=False,
include=include,
exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0))
data = epochs.get_data()
times = epochs.times
nave = len(data)
epochs_nopicks = Epochs(raw,
events,
event_id,
tmin,
tmax,
baseline=(None, 0))
freqs = np.arange(6, 20, 5) # define frequencies of interest
n_cycles = freqs / 4.
# Test first with a single epoch
power, itc = tfr_morlet(epochs[0],
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True)
# Now compute evoked
evoked = epochs.average()
power_evoked = tfr_morlet(evoked,
freqs,
n_cycles,
use_fft=True,
return_itc=False)
assert_raises(ValueError, tfr_morlet, evoked, freqs, 1., return_itc=True)
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True)
# Test picks argument
power_picks, itc_picks = tfr_morlet(epochs_nopicks,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True,
picks=picks)
# the actual data arrays here are equivalent, too...
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(itc.data, itc_picks.data)
assert_array_almost_equal(power.data, power_evoked.data)
print(itc) # test repr
print(itc.ch_names) # test property
itc += power # test add
itc -= power # test add
power.apply_baseline(baseline=(-0.1, 0), mode='logratio')
assert_true('meg' in power)
assert_true('grad' in power)
assert_false('mag' in power)
assert_false('eeg' in power)
assert_equal(power.nave, nave)
assert_equal(itc.nave, nave)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
# grand average
itc2 = itc.copy()
itc2.info['bads'] = [itc2.ch_names[0]] # test channel drop
gave = grand_average([itc2, itc])
assert_equal(
gave.data.shape,
(itc2.data.shape[0] - 1, itc2.data.shape[1], itc2.data.shape[2]))
assert_equal(itc2.ch_names[1:], gave.ch_names)
assert_equal(gave.nave, 2)
itc2.drop_channels(itc2.info["bads"])
assert_array_almost_equal(gave.data, itc2.data)
itc2.data = np.ones(itc2.data.shape)
itc.data = np.zeros(itc.data.shape)
itc2.nave = 2
itc.nave = 1
itc.drop_channels([itc.ch_names[0]])
combined_itc = combine_tfr([itc2, itc])
assert_array_almost_equal(combined_itc.data,
np.ones(combined_itc.data.shape) * 2 / 3)
# more tests
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=2,
use_fft=False,
return_itc=True)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
Fs = raw.info['sfreq'] # sampling in Hz
tfr = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2)
assert_true(tfr.shape == (len(picks), len(freqs), len(times)))
single_power = single_trial_power(data,
Fs,
freqs,
use_fft=False,
n_cycles=2)
assert_array_almost_equal(np.mean(single_power), power.data)
power_pick = power.pick_channels(power.ch_names[:10:2])
assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
power_drop = power.drop_channels(power.ch_names[1:10:2])
assert_equal(power_drop.ch_names, power_pick.ch_names)
assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))
mne.equalize_channels([power_pick, power_drop])
assert_equal(power_pick.ch_names, power_drop.ch_names)
assert_equal(power_pick.data.shape, power_drop.data.shape)
# Time vector
times = 1e3 * epochs.times # change unit to ms
# Factor to downs-sample the temporal dimension of the PSD computed by
# single_trial_power.
decim = 2
frequencies = np.arange(7, 30, 3) # define frequencies of interest
Fs = raw.info['sfreq'] # sampling in Hz
n_cycles = frequencies / frequencies[0]
baseline_mask = times[::decim] < 0
# now create TFR representations for all conditions
epochs_power = []
for condition in [epochs[k].get_data()[:, 97:98, :] for k in event_id]:
this_power = single_trial_power(condition, Fs=Fs, frequencies=frequencies,
n_cycles=n_cycles, use_fft=False,
decim=decim)
this_power = this_power[:, 0, :, :] # we only have one channel.
# Compute ratio with baseline power (be sure to correct time vector with
# decimation factor)
epochs_baseline = np.mean(this_power[:, :, baseline_mask], axis=2)
this_power /= epochs_baseline[..., np.newaxis]
epochs_power.append(this_power)
###############################################################################
# Setup repeated measures ANOVA
n_conditions = len(epochs.event_id)
n_replications = epochs.events.shape[0] / n_conditions
# we will tell the ANOVA how to interpret the data matrix in terms of
# factors. This done via the factor levels argument which is a list
def test_compute_tfr():
"""Test _compute_tfr function"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.498 # Allows exhaustive decimation testing
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = read_events(event_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False,
stim=False, include=[], exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
data = epochs.get_data()
sfreq = epochs.info['sfreq']
freqs = np.arange(10, 20, 3).astype(float)
# Check all combination of options
for method, use_fft, zero_mean, output in product(
('multitaper', 'morlet'), (False, True), (False, True),
('complex', 'power', 'phase',
'avg_power_itc', 'avg_power', 'itc')):
# Check exception
if (method == 'multitaper') and (output == 'phase'):
assert_raises(NotImplementedError, _compute_tfr, data, freqs,
sfreq, method=method, output=output)
continue
# Check runs
out = _compute_tfr(data, freqs, sfreq, method=method,
use_fft=use_fft, zero_mean=zero_mean,
n_cycles=2., output=output)
# Check shapes
shape = np.r_[data.shape[:2], len(freqs), data.shape[2]]
if ('avg' in output) or ('itc' in output):
assert_array_equal(shape[1:], out.shape)
else:
assert_array_equal(shape, out.shape)
# Check types
if output in ('complex', 'avg_power_itc'):
assert_equal(np.complex, out.dtype)
else:
assert_equal(np.float, out.dtype)
assert_true(np.all(np.isfinite(out)))
# Check that functions are equivalent to
# i) single_trial_power: X, shape (n_signals, n_chans, n_times)
old_power = single_trial_power(data, sfreq, freqs, n_cycles=2.)
new_power = _compute_tfr(data, freqs, sfreq, n_cycles=2.,
method='morlet', output='power')
assert_array_almost_equal(old_power, new_power)
old_power = single_trial_power(data, sfreq, freqs, n_cycles=2.,
times=epochs.times, baseline=(-.100, 0),
baseline_mode='ratio')
new_power = rescale(new_power, epochs.times, (-.100, 0), 'ratio')
# ii) cwt_morlet: X, shape (n_signals, n_times)
old_complex = cwt_morlet(data[0], sfreq, freqs, n_cycles=2.)
new_complex = _compute_tfr(data[[0]], freqs, sfreq, n_cycles=2.,
method='morlet', output='complex')
assert_array_almost_equal(old_complex, new_complex[0])
# Check errors params
for _data in (None, 'foo', data[0]):
assert_raises(ValueError, _compute_tfr, _data, freqs, sfreq)
for _freqs in (None, 'foo', [[0]]):
assert_raises(ValueError, _compute_tfr, data, _freqs, sfreq)
for _sfreq in (None, 'foo'):
assert_raises(ValueError, _compute_tfr, data, freqs, _sfreq)
for key in ('output', 'method', 'use_fft', 'decim', 'n_jobs'):
for value in (None, 'foo'):
kwargs = {key: value} # FIXME pep8
assert_raises(ValueError, _compute_tfr, data, freqs, sfreq,
**kwargs)
# No time_bandwidth param in morlet
assert_raises(ValueError, _compute_tfr, data, freqs, sfreq,
method='morlet', time_bandwidth=1)
# No phase in multitaper XXX Check ?
assert_raises(NotImplementedError, _compute_tfr, data, freqs, sfreq,
method='multitaper', output='phase')
# Inter-trial coherence tests
out = _compute_tfr(data, freqs, sfreq, output='itc', n_cycles=2.)
assert_true(np.sum(out >= 1) == 0)
assert_true(np.sum(out <= 0) == 0)
# Check decim shapes
# 2: multiple of len(times) even
# 3: multiple odd
# 8: not multiple, even
# 9: not multiple, odd
for decim in (2, 3, 8, 9, slice(0, 2), slice(1, 3), slice(2, 4)):
_decim = slice(None, None, decim) if isinstance(decim, int) else decim
n_time = len(np.arange(data.shape[2])[_decim])
shape = np.r_[data.shape[:2], len(freqs), n_time]
for method in ('multitaper', 'morlet'):
# Single trials
out = _compute_tfr(data, freqs, sfreq, method=method,
decim=decim, n_cycles=2.)
assert_array_equal(shape, out.shape)
# Averages
out = _compute_tfr(data, freqs, sfreq, method=method,
decim=decim, output='avg_power',
n_cycles=2.)
assert_array_equal(shape[1:], out.shape)
def test_time_frequency():
"""Test the to-be-deprecated time frequency transform (PSD and ITC)"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.498 # Allows exhaustive decimation testing
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False,
stim=False, include=include, exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
data = epochs.get_data()
times = epochs.times
nave = len(data)
epochs_nopicks = Epochs(raw, events, event_id, tmin, tmax,
baseline=(None, 0))
freqs = np.arange(6, 20, 5) # define frequencies of interest
n_cycles = freqs / 4.
# Test first with a single epoch
power, itc = tfr_morlet(epochs[0], freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=True)
# Now compute evoked
evoked = epochs.average()
power_evoked = tfr_morlet(evoked, freqs, n_cycles, use_fft=True,
return_itc=False)
assert_raises(ValueError, tfr_morlet, evoked, freqs, 1., return_itc=True)
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=True)
power_, itc_ = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=True, decim=slice(0, 2))
# Test picks argument and average parameter
assert_raises(ValueError, tfr_morlet, epochs, freqs=freqs,
n_cycles=n_cycles, return_itc=True, average=False)
power_picks, itc_picks = \
tfr_morlet(epochs_nopicks,
freqs=freqs, n_cycles=n_cycles, use_fft=True,
return_itc=True, picks=picks, average=True)
epochs_power_picks = \
tfr_morlet(epochs_nopicks,
freqs=freqs, n_cycles=n_cycles, use_fft=True,
return_itc=False, picks=picks, average=False)
power_picks_avg = epochs_power_picks.average()
# the actual data arrays here are equivalent, too...
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(power.data, power_picks_avg.data)
assert_array_almost_equal(itc.data, itc_picks.data)
assert_array_almost_equal(power.data, power_evoked.data)
print(itc) # test repr
print(itc.ch_names) # test property
itc += power # test add
itc -= power # test add
power.apply_baseline(baseline=(-0.1, 0), mode='logratio')
assert_true('meg' in power)
assert_true('grad' in power)
assert_false('mag' in power)
assert_false('eeg' in power)
assert_equal(power.nave, nave)
assert_equal(itc.nave, nave)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(power_.data.shape == (len(picks), len(freqs), 2))
assert_true(power_.data.shape == itc_.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
# grand average
itc2 = itc.copy()
itc2.info['bads'] = [itc2.ch_names[0]] # test channel drop
gave = grand_average([itc2, itc])
assert_equal(gave.data.shape, (itc2.data.shape[0] - 1,
itc2.data.shape[1],
itc2.data.shape[2]))
assert_equal(itc2.ch_names[1:], gave.ch_names)
assert_equal(gave.nave, 2)
itc2.drop_channels(itc2.info["bads"])
assert_array_almost_equal(gave.data, itc2.data)
itc2.data = np.ones(itc2.data.shape)
itc.data = np.zeros(itc.data.shape)
itc2.nave = 2
itc.nave = 1
itc.drop_channels([itc.ch_names[0]])
combined_itc = combine_tfr([itc2, itc])
assert_array_almost_equal(combined_itc.data,
np.ones(combined_itc.data.shape) * 2 / 3)
# more tests
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2, use_fft=False,
return_itc=True)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
Fs = raw.info['sfreq'] # sampling in Hz
tfr = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2)
assert_true(tfr.shape == (len(picks), len(freqs), len(times)))
tfr2 = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2,
decim=slice(0, 2))
assert_true(tfr2.shape == (len(picks), len(freqs), 2))
single_power = single_trial_power(data, Fs, freqs, use_fft=False,
n_cycles=2)
single_power2 = single_trial_power(data, Fs, freqs, use_fft=False,
n_cycles=2, decim=slice(0, 2))
single_power3 = single_trial_power(data, Fs, freqs, use_fft=False,
n_cycles=2, decim=slice(1, 3))
single_power4 = single_trial_power(data, Fs, freqs, use_fft=False,
n_cycles=2, decim=slice(2, 4))
assert_array_almost_equal(np.mean(single_power, axis=0), power.data)
assert_array_almost_equal(np.mean(single_power2, axis=0),
power.data[:, :, :2])
assert_array_almost_equal(np.mean(single_power3, axis=0),
power.data[:, :, 1:3])
assert_array_almost_equal(np.mean(single_power4, axis=0),
power.data[:, :, 2:4])
power_pick = power.pick_channels(power.ch_names[:10:2])
assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
power_drop = power.drop_channels(power.ch_names[1:10:2])
assert_equal(power_drop.ch_names, power_pick.ch_names)
assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))
mne.equalize_channels([power_pick, power_drop])
assert_equal(power_pick.ch_names, power_drop.ch_names)
assert_equal(power_pick.data.shape, power_drop.data.shape)
# Test decimation:
# 2: multiple of len(times) even
# 3: multiple odd
# 8: not multiple, even
# 9: not multiple, odd
for decim in [2, 3, 8, 9]:
for use_fft in [True, False]:
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2,
use_fft=use_fft, return_itc=True,
decim=decim)
assert_equal(power.data.shape[2],
np.ceil(float(len(times)) / decim))
freqs = range(50, 55)
decim = 2
_, n_chan, n_time = data.shape
tfr = cwt_morlet(data[0, :, :], sfreq=epochs.info['sfreq'],
freqs=freqs, decim=decim)
assert_equal(tfr.shape, (n_chan, len(freqs), n_time // decim))
# Test cwt modes
Ws = morlet(512, [10, 20], n_cycles=2)
assert_raises(ValueError, cwt, data[0, :, :], Ws, mode='foo')
for use_fft in [True, False]:
for mode in ['same', 'valid', 'full']:
# XXX JRK: full wavelet decomposition needs to be implemented
if (not use_fft) and mode == 'full':
assert_raises(ValueError, cwt, data[0, :, :], Ws,
use_fft=use_fft, mode=mode)
continue
cwt(data[0, :, :], Ws, use_fft=use_fft, mode=mode)
# Test decim parameter checks
assert_raises(TypeError, single_trial_power, data, Fs, freqs,
use_fft=False, n_cycles=2, decim=None)
assert_raises(TypeError, tfr_morlet, epochs, freqs=freqs,
n_cycles=n_cycles, use_fft=True, return_itc=True,
decim='decim')
def transform(self, X):
return single_trial_power(X, self.sfreq, self.frequencies,
n_cycles=self.n_cycles, decim=self.decim,
n_jobs=self.n_jobs)
# Factor to down-sample the temporal dimension of the PSD computed by
# single_trial_power.
decim = 2
frequencies = np.arange(7, 30, 3) # define frequencies of interest
sfreq = raw.info['sfreq'] # sampling in Hz
n_cycles = frequencies / frequencies[0]
baseline_mask = times[::decim] < 0
###############################################################################
# Create TFR representations for all conditions
# ---------------------------------------------
epochs_power = list()
for condition in [epochs[k].get_data()[:, 97:98, :] for k in event_id]:
this_power = single_trial_power(condition,
sfreq=sfreq,
frequencies=frequencies,
n_cycles=n_cycles,
decim=decim)
this_power = this_power[:, 0, :, :] # we only have one channel.
# Compute ratio with baseline power (be sure to correct time vector with
# decimation factor)
epochs_baseline = np.mean(this_power[:, :, baseline_mask], axis=2)
this_power /= epochs_baseline[..., np.newaxis]
epochs_power.append(this_power)
###############################################################################
# Setup repeated measures ANOVA
# -----------------------------
#
# We will tell the ANOVA how to interpret the data matrix in terms of factors.
# This is done via the factor levels argument which is a list of the number
# evoked_data = np.mean(data, 0)
# Factor to down-sample the temporal dimension of the PSD computed by
# single_trial_power. Decimation occurs after frequency decomposition and can
# be used to reduce memory usage (and possibly computational time of downstream
# operations such as nonparametric statistics) if you don't need high
# spectrotemporal resolution.
decim = 5
frequencies = np.arange(8, 40, 2) # define frequencies of interest
sfreq = raw.info["sfreq"] # sampling in Hz
epochs_power = single_trial_power(
data,
sfreq=sfreq,
frequencies=frequencies,
n_cycles=4,
use_fft=False,
n_jobs=1,
baseline=(-100, 0),
times=times,
baseline_mode="ratio",
decim=decim,
)
# Crop in time to keep only what is between 0 and 400 ms
time_mask = (times > 0) & (times < 400)
evoked_data = evoked_data[:, time_mask]
times = times[time_mask]
# The time vector reflects the original time points, not the decimated time
# points returned by single trial power. Be sure to decimate the time mask
# appropriately.
epochs_power = epochs_power[..., time_mask[::decim]]
# data -= evoked_data[None,:,:] # remove evoked component
# evoked_data = np.mean(data, 0)
# Factor to down-sample the temporal dimension of the PSD computed by
# single_trial_power. Decimation occurs after frequency decomposition and can
# be used to reduce memory usage (and possibly computational time of downstream
# operations such as nonparametric statistics) if you don't need high
# spectrotemporal resolution.
decim = 5
frequencies = np.arange(8, 40, 2) # define frequencies of interest
Fs = raw.info['sfreq'] # sampling in Hz
epochs_power = single_trial_power(data,
Fs=Fs,
frequencies=frequencies,
n_cycles=4,
use_fft=False,
n_jobs=1,
baseline=(-100, 0),
times=times,
baseline_mode='ratio',
decim=decim)
# Crop in time to keep only what is between 0 and 400 ms
time_mask = (times > 0) & (times < 400)
evoked_data = evoked_data[:, time_mask]
times = times[time_mask]
# The time vector reflects the original time points, not the decimated time
# points returned by single trial power. Be sure to decimate the time mask
# appropriately.
epochs_power = epochs_power[..., time_mask[::decim]]
def test_time_frequency():
"""Test time frequency transform (PSD and phase lock)
"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = io.Raw(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False,
stim=False, include=include, exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
data = epochs.get_data()
times = epochs.times
nave = len(data)
freqs = np.arange(6, 20, 5) # define frequencies of interest
n_cycles = freqs / 4.
# Test first with a single epoch
power, itc = tfr_morlet(epochs[0], freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=True)
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=True)
print(itc) # test repr
print(itc.ch_names) # test property
itc = itc + power # test add
itc = itc - power # test add
itc -= power
itc += power
power.apply_baseline(baseline=(-0.1, 0), mode='logratio')
assert_true('meg' in power)
assert_true('grad' in power)
assert_false('mag' in power)
assert_false('eeg' in power)
assert_equal(power.nave, nave)
assert_equal(itc.nave, nave)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2, use_fft=False,
return_itc=True)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
Fs = raw.info['sfreq'] # sampling in Hz
tfr = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2)
assert_true(tfr.shape == (len(picks), len(freqs), len(times)))
single_power = single_trial_power(data, Fs, freqs, use_fft=False,
n_cycles=2)
assert_array_almost_equal(np.mean(single_power), power.data)
power_pick = power.pick_channels(power.ch_names[:10:2])
assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
power_drop = power.drop_channels(power.ch_names[1:10:2])
assert_equal(power_drop.ch_names, power_pick.ch_names)
assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))
mne.equalize_channels([power_pick, power_drop])
assert_equal(power_pick.ch_names, power_drop.ch_names)
assert_equal(power_pick.data.shape, power_drop.data.shape)
evoked_data = np.mean(data, 0)
# data -= evoked_data[None,:,:] # remove evoked component
# evoked_data = np.mean(data, 0)
# Factor to down-sample the temporal dimension of the PSD computed by
# single_trial_power. Decimation occurs after frequency decomposition and can
# be used to reduce memory usage (and possibly computational time of downstream
# operations such as nonparametric statistics) if you don't need high
# spectrotemporal resolution.
decim = 5
frequencies = np.arange(8, 40, 2) # define frequencies of interest
sfreq = raw.info['sfreq'] # sampling in Hz
epochs_power = single_trial_power(data, sfreq=sfreq, frequencies=frequencies,
n_cycles=4, n_jobs=1,
baseline=(-100, 0), times=times,
baseline_mode='ratio', decim=decim)
# Crop in time to keep only what is between 0 and 400 ms
time_mask = (times > 0) & (times < 400)
evoked_data = evoked_data[:, time_mask]
times = times[time_mask]
# The time vector reflects the original time points, not the decimated time
# points returned by single trial power. Be sure to decimate the time mask
# appropriately.
epochs_power = epochs_power[..., time_mask[::decim]]
epochs_power = epochs_power[:, 0, :, :]
epochs_power = np.log10(epochs_power) # take log of ratio
# under the null hypothesis epochs_power should be now be 0
reject=reject)
data_condition_2 = epochs_condition_2.get_data() # as 3D matrix
data_condition_2 *= 1e13 # change unit to fT / cm
# Take only one channel
data_condition_1 = data_condition_1[:, 97:98, :]
data_condition_2 = data_condition_2[:, 97:98, :]
# Time vector
times = 1e3 * epochs_condition_1.times # change unit to ms
frequencies = np.arange(7, 30, 3) # define frequencies of interest
Fs = raw.info['sfreq'] # sampling in Hz
n_cycles = 1.5
epochs_power_1 = single_trial_power(data_condition_1, Fs=Fs,
frequencies=frequencies,
n_cycles=n_cycles, use_fft=False)
epochs_power_2 = single_trial_power(data_condition_2, Fs=Fs,
frequencies=frequencies,
n_cycles=n_cycles, use_fft=False)
epochs_power_1 = epochs_power_1[:, 0, :, :] # only 1 channel to get 3D matrix
epochs_power_2 = epochs_power_2[:, 0, :, :] # only 1 channel to get 3D matrix
# do ratio with baseline power:
epochs_power_1 /= np.mean(epochs_power_1[:, :, times < 0], axis=2)[:, :, None]
epochs_power_2 /= np.mean(epochs_power_2[:, :, times < 0], axis=2)[:, :, None]
###############################################################################
# Compute statistic
def test_compute_tfr():
"""Test _compute_tfr function"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.498 # Allows exhaustive decimation testing
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = read_events(event_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False,
stim=False, include=[], exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
data = epochs.get_data()
sfreq = epochs.info['sfreq']
freqs = np.arange(10, 20, 3).astype(float)
# Check all combination of options
for method, use_fft, zero_mean, output in product(
('multitaper', 'morlet'), (False, True), (False, True),
('complex', 'power', 'phase',
'avg_power_itc', 'avg_power', 'itc')):
# Check exception
if (method == 'multitaper') and (output == 'phase'):
assert_raises(NotImplementedError, _compute_tfr, data, freqs,
sfreq, method=method, output=output)
continue
# Check runs
out = _compute_tfr(data, freqs, sfreq, method=method,
use_fft=use_fft, zero_mean=zero_mean,
n_cycles=2., output=output)
# Check shapes
shape = np.r_[data.shape[:2], len(freqs), data.shape[2]]
if ('avg' in output) or ('itc' in output):
assert_array_equal(shape[1:], out.shape)
else:
assert_array_equal(shape, out.shape)
# Check types
if output in ('complex', 'avg_power_itc'):
assert_equal(np.complex, out.dtype)
else:
assert_equal(np.float, out.dtype)
assert_true(np.all(np.isfinite(out)))
# Check that functions are equivalent to
# i) single_trial_power: X, shape (n_signals, n_chans, n_times)
old_power = single_trial_power(data, sfreq, freqs, n_cycles=2.)
new_power = _compute_tfr(data, freqs, sfreq, n_cycles=2.,
method='morlet', output='power')
assert_array_almost_equal(old_power, new_power)
old_power = single_trial_power(data, sfreq, freqs, n_cycles=2.,
times=epochs.times, baseline=(-.100, 0),
baseline_mode='ratio')
new_power = rescale(new_power, epochs.times, (-.100, 0), 'ratio')
# ii) cwt_morlet: X, shape (n_signals, n_times)
old_complex = cwt_morlet(data[0], sfreq, freqs, n_cycles=2.)
new_complex = _compute_tfr(data[[0]], freqs, sfreq, n_cycles=2.,
method='morlet', output='complex')
assert_array_almost_equal(old_complex, new_complex[0])
# Check errors params
for _data in (None, 'foo', data[0]):
assert_raises(ValueError, _compute_tfr, _data, freqs, sfreq)
for _freqs in (None, 'foo', [[0]]):
assert_raises(ValueError, _compute_tfr, data, _freqs, sfreq)
for _sfreq in (None, 'foo'):
assert_raises(ValueError, _compute_tfr, data, freqs, _sfreq)
for key in ('output', 'method', 'use_fft', 'decim', 'n_jobs'):
for value in (None, 'foo'):
kwargs = {key: value} # FIXME pep8
assert_raises(ValueError, _compute_tfr, data, freqs, sfreq,
**kwargs)
# No time_bandwidth param in morlet
assert_raises(ValueError, _compute_tfr, data, freqs, sfreq,
method='morlet', time_bandwidth=1)
# No phase in multitaper XXX Check ?
assert_raises(NotImplementedError, _compute_tfr, data, freqs, sfreq,
method='multitaper', output='phase')
# Inter-trial coherence tests
out = _compute_tfr(data, freqs, sfreq, output='itc', n_cycles=2.)
assert_true(np.sum(out >= 1) == 0)
assert_true(np.sum(out <= 0) == 0)
# Check decim shapes
# 2: multiple of len(times) even
# 3: multiple odd
# 8: not multiple, even
# 9: not multiple, odd
for decim in (2, 3, 8, 9, slice(0, 2), slice(1, 3), slice(2, 4)):
_decim = slice(None, None, decim) if isinstance(decim, int) else decim
n_time = len(np.arange(data.shape[2])[_decim])
shape = np.r_[data.shape[:2], len(freqs), n_time]
for method in ('multitaper', 'morlet'):
# Single trials
out = _compute_tfr(data, freqs, sfreq, method=method,
decim=decim, n_cycles=2.)
assert_array_equal(shape, out.shape)
# Averages
out = _compute_tfr(data, freqs, sfreq, method=method,
decim=decim, output='avg_power',
n_cycles=2.)
assert_array_equal(shape[1:], out.shape)
evoked_data = np.mean(data, 0)
# data -= evoked_data[None,:,:] # remove evoked component
# evoked_data = np.mean(data, 0)
# Factor to down-sample the temporal dimension of the PSD computed by
# single_trial_power. Decimation occurs after frequency decomposition and can
# be used to reduce memory usage (and possibly computational time of downstream
# operations such as nonparametric statistics) if you don't need high
# spectrotemporal resolution.
decim = 5
frequencies = np.arange(8, 40, 2) # define frequencies of interest
sfreq = raw.info['sfreq'] # sampling in Hz
epochs_power = single_trial_power(data, sfreq=sfreq, frequencies=frequencies,
n_cycles=4, n_jobs=1,
baseline=(-100, 0), times=times,
baseline_mode='ratio', decim=decim)
# Crop in time to keep only what is between 0 and 400 ms
time_mask = (times > 0) & (times < 400)
evoked_data = evoked_data[:, time_mask]
times = times[time_mask]
# The time vector reflects the original time points, not the decimated time
# points returned by single trial power. Be sure to decimate the time mask
# appropriately.
epochs_power = epochs_power[..., time_mask[::decim]]
epochs_power = epochs_power[:, 0, :, :]
epochs_power = np.log10(epochs_power) # take log of ratio
# under the null hypothesis epochs_power should be now be 0
# Time vector
times = 1e3 * epochs.times # change unit to ms
# Take only one channel
ch_name = raw.info['ch_names'][97]
data = data[:, 97:98, :]
evoked_data = np.mean(data, 0)
# data -= evoked_data[None,:,:] # remove evoked component
# evoked_data = np.mean(data, 0)
frequencies = np.arange(8, 40, 2) # define frequencies of interest
Fs = raw.info['sfreq'] # sampling in Hz
epochs_power = single_trial_power(data, Fs=Fs, frequencies=frequencies,
n_cycles=4, use_fft=False, n_jobs=1,
baseline=(-100, 0), times=times,
baseline_mode='ratio')
# Crop in time to keep only what is between 0 and 400 ms
time_mask = (times > 0) & (times < 400)
epochs_power = epochs_power[:, :, :, time_mask]
evoked_data = evoked_data[:, time_mask]
times = times[time_mask]
epochs_power = epochs_power[:, 0, :, :]
epochs_power = np.log10(epochs_power) # take log of ratio
# under the null hypothesis epochs_power should be now be 0
###############################################################################
# Compute statistic
threshold = 2.5
# Time vector
times = 1e3 * epochs_condition_1.times # change unit to ms
# Factor to downsample the temporal dimension of the PSD computed by
# single_trial_power. Decimation occurs after frequency decomposition and can
# be used to reduce memory usage (and possibly comptuational time of downstream
# operations such as nonparametric statistics) if you don't need high
# spectrotemporal resolution.
decim = 2
frequencies = np.arange(7, 30, 3) # define frequencies of interest
sfreq = raw.info['sfreq'] # sampling in Hz
n_cycles = 1.5
epochs_power_1 = single_trial_power(data_condition_1, sfreq=sfreq,
frequencies=frequencies,
n_cycles=n_cycles, decim=decim)
epochs_power_2 = single_trial_power(data_condition_2, sfreq=sfreq,
frequencies=frequencies,
n_cycles=n_cycles, decim=decim)
epochs_power_1 = epochs_power_1[:, 0, :, :] # only 1 channel to get 3D matrix
epochs_power_2 = epochs_power_2[:, 0, :, :] # only 1 channel to get 3D matrix
# Compute ratio with baseline power (be sure to correct time vector with
# decimation factor)
baseline_mask = times[::decim] < 0
epochs_baseline_1 = np.mean(epochs_power_1[:, :, baseline_mask], axis=2)
epochs_power_1 /= epochs_baseline_1[..., np.newaxis]
epochs_baseline_2 = np.mean(epochs_power_2[:, :, baseline_mask], axis=2)
def test_time_frequency():
"""Test time frequency transform (PSD and phase lock)
"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = io.Raw(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False,
stim=False, include=include, exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
data = epochs.get_data()
times = epochs.times
nave = len(data)
epochs_nopicks = Epochs(raw, events, event_id, tmin, tmax,
baseline=(None, 0))
freqs = np.arange(6, 20, 5) # define frequencies of interest
n_cycles = freqs / 4.
# Test first with a single epoch
power, itc = tfr_morlet(epochs[0], freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=True)
# Now compute evoked
evoked = epochs.average()
power_evoked = tfr_morlet(evoked, freqs, n_cycles, use_fft=True,
return_itc=False)
assert_raises(ValueError, tfr_morlet, evoked, freqs, 1., return_itc=True)
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=True)
# Test picks argument
power_picks, itc_picks = tfr_morlet(epochs_nopicks, freqs=freqs,
n_cycles=n_cycles, use_fft=True,
return_itc=True, picks=picks)
# the actual data arrays here are equivalent, too...
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(itc.data, itc_picks.data)
assert_array_almost_equal(power.data, power_evoked.data)
print(itc) # test repr
print(itc.ch_names) # test property
itc += power # test add
itc -= power # test add
power.apply_baseline(baseline=(-0.1, 0), mode='logratio')
assert_true('meg' in power)
assert_true('grad' in power)
assert_false('mag' in power)
assert_false('eeg' in power)
assert_equal(power.nave, nave)
assert_equal(itc.nave, nave)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
# grand average
itc2 = itc.copy()
itc2.info['bads'] = [itc2.ch_names[0]] # test channel drop
gave = grand_average([itc2, itc])
assert_equal(gave.data.shape, (itc2.data.shape[0] - 1,
itc2.data.shape[1],
itc2.data.shape[2]))
assert_equal(itc2.ch_names[1:], gave.ch_names)
assert_equal(gave.nave, 2)
itc2.drop_channels(itc2.info["bads"])
assert_array_almost_equal(gave.data, itc2.data)
itc2.data = np.ones(itc2.data.shape)
itc.data = np.zeros(itc.data.shape)
itc2.nave = 2
itc.nave = 1
itc.drop_channels([itc.ch_names[0]])
combined_itc = combine_tfr([itc2, itc])
assert_array_almost_equal(combined_itc.data,
np.ones(combined_itc.data.shape) * 2 / 3)
# more tests
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2, use_fft=False,
return_itc=True)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
Fs = raw.info['sfreq'] # sampling in Hz
tfr = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2)
assert_true(tfr.shape == (len(picks), len(freqs), len(times)))
single_power = single_trial_power(data, Fs, freqs, use_fft=False,
n_cycles=2)
assert_array_almost_equal(np.mean(single_power), power.data)
power_pick = power.pick_channels(power.ch_names[:10:2])
assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
power_drop = power.drop_channels(power.ch_names[1:10:2])
assert_equal(power_drop.ch_names, power_pick.ch_names)
assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))
mne.equalize_channels([power_pick, power_drop])
assert_equal(power_pick.ch_names, power_drop.ch_names)
assert_equal(power_pick.data.shape, power_drop.data.shape)
# Test decimation
for decim in [2, 3]:
for use_fft in [True, False]:
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2,
use_fft=use_fft, return_itc=True,
decim=decim)
assert_equal(power.data.shape[2],
np.ceil(float(len(times)) / decim))
# Test cwt modes
Ws = morlet(512, [10, 20], n_cycles=2)
assert_raises(ValueError, cwt, data[0, :, :], Ws, mode='foo')
for use_fft in [True, False]:
for mode in ['same', 'valid', 'full']:
# XXX JRK: full wavelet decomposition needs to be implemented
if (not use_fft) and mode == 'full':
assert_raises(ValueError, cwt, data[0, :, :], Ws,
use_fft=use_fft, mode=mode)
continue
cwt(data[0, :, :], Ws, use_fft=use_fft, mode=mode)
