def test_decim():
"""Test evoked decimation."""
rng = np.random.RandomState(0)
n_channels, n_times = 10, 20
dec_1, dec_2 = 2, 3
decim = dec_1 * dec_2
sfreq = 10.
sfreq_new = sfreq / decim
data = rng.randn(n_channels, n_times)
info = create_info(n_channels, sfreq, 'eeg')
with info._unlock():
info['lowpass'] = sfreq_new / float(decim)
evoked = EvokedArray(data, info, tmin=-1)
evoked_dec = evoked.copy().decimate(decim)
evoked_dec_2 = evoked.copy().decimate(decim, offset=1)
evoked_dec_3 = evoked.decimate(dec_1).decimate(dec_2)
assert_array_equal(evoked_dec.data, data[:, ::decim])
assert_array_equal(evoked_dec_2.data, data[:, 1::decim])
assert_array_equal(evoked_dec.data, evoked_dec_3.data)
# Check proper updating of various fields
assert evoked_dec.first == -2
assert evoked_dec.last == 1
assert_array_equal(evoked_dec.times, [-1, -0.4, 0.2, 0.8])
assert evoked_dec_2.first == -2
assert evoked_dec_2.last == 1
assert_array_equal(evoked_dec_2.times, [-0.9, -0.3, 0.3, 0.9])
assert evoked_dec_3.first == -2
assert evoked_dec_3.last == 1
assert_array_equal(evoked_dec_3.times, [-1, -0.4, 0.2, 0.8])
# make sure the time nearest zero is also sample number 0.
for ev in (evoked_dec, evoked_dec_2, evoked_dec_3):
lowest_index = np.argmin(np.abs(np.arange(ev.first, ev.last)))
idxs_of_times_nearest_zero = \
np.where(np.abs(ev.times) == np.min(np.abs(ev.times)))[0]
# we use `in` here in case two times are equidistant from 0.
assert lowest_index in idxs_of_times_nearest_zero
assert len(idxs_of_times_nearest_zero) in (1, 2)
# Now let's do it with some real data
raw = read_raw_fif(raw_fname)
events = read_events(event_name)
sfreq_new = raw.info['sfreq'] / decim
with raw.info._unlock():
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)
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_decim():
"""Test evoked decimation."""
rng = np.random.RandomState(0)
n_channels, n_times = 10, 20
dec_1, dec_2 = 2, 3
decim = dec_1 * dec_2
sfreq = 10.
sfreq_new = sfreq / decim
data = rng.randn(n_channels, n_times)
info = create_info(n_channels, sfreq, 'eeg')
info['lowpass'] = sfreq_new / float(decim)
evoked = EvokedArray(data, info, tmin=-1)
evoked_dec = evoked.copy().decimate(decim)
evoked_dec_2 = evoked.copy().decimate(decim, offset=1)
evoked_dec_3 = evoked.decimate(dec_1).decimate(dec_2)
assert_array_equal(evoked_dec.data, data[:, ::decim])
assert_array_equal(evoked_dec_2.data, data[:, 1::decim])
assert_array_equal(evoked_dec.data, evoked_dec_3.data)
# Check proper updating of various fields
assert evoked_dec.first == -1
assert evoked_dec.last == 2
assert_array_equal(evoked_dec.times, [-1, -0.4, 0.2, 0.8])
assert evoked_dec_2.first == -1
assert evoked_dec_2.last == 2
assert_array_equal(evoked_dec_2.times, [-0.9, -0.3, 0.3, 0.9])
assert evoked_dec_3.first == -1
assert evoked_dec_3.last == 2
assert_array_equal(evoked_dec_3.times, [-1, -0.4, 0.2, 0.8])
# Now let's do it with some real data
raw = read_raw_fif(raw_fname)
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)
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 _prepare_epochs_data(self, epochs, top_min, top_max):
ep_data = epochs.get_data()
evoked = EvokedArray(ep_data[0], epochs.info)
if self.fourier is False:
self._get_topographies(evoked, top_min, top_max)
temp_list = list()
for ie, _e in enumerate(ep_data):
if self.subsample is not None:
if ie == 0:
print('Subsampling data with step {0}'.format(
self.subsample))
temp_list.append(_e[:, self.s_min:self.s_max +
1:self.subsample])
else:
temp_list.append(_e[:, self.s_min:self.s_max + 1])
return np.hstack(temp_list)
elif self.fourier is True:
tstep = 1 / evoked.info['sfreq']
evoked_f = evoked.copy()
evoked_f.data *= np.hamming(evoked.data.shape[1])
evoked_f.data = (np.fft.rfft(evoked_f.data))
freqs = np.fft.rfftfreq(evoked.data.shape[1], tstep)
print('Data have been converted to the frequency domain.')
self._get_topographies(evoked_f, top_min, top_max, freqs=freqs)
temp_list = list()
temp_list2 = list()
for ie, _e in enumerate(ep_data):
_e *= np.hamming(_e.shape[1])
_e_f = np.fft.rfft(_e)
if self.subsample is not None:
if ie == 0:
print('Subsampling data with step {0}'.format(
self.subsample))
temp_list.append(_e_f[:, self.s_min:self.s_max +
1:self.subsample])
else:
temp_list.append(_e_f[:, self.s_min:self.s_max + 1])
for _data_temp in temp_list:
for l in _data_temp.T:
temp_list2.append(
np.vstack([np.real(l), np.imag(l)]).T)
return np.hstack(temp_list2)
else:
raise ValueError
