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# Author: Jean-Remi King, <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
from numpy.testing import assert_array_equal
import pytest
from mne.utils import requires_sklearn
from mne.decoding.time_frequency import TimeFrequency
@requires_sklearn
def test_timefrequency():
"""Test TimeFrequency."""
from sklearn.base import clone
# Init
n_freqs = 3
freqs = np.linspace(20, 30, n_freqs)
tf = TimeFrequency(freqs, sfreq=100)
for output in ['avg_power', 'foo', None]:
pytest.raises(ValueError, TimeFrequency, freqs, output=output)
tf = clone(tf)
# Fit
n_epochs, n_chans, n_times = 10, 2, 100
X = np.random.rand(n_epochs, n_chans, n_times)
tf.fit(X, None)
# Transform
tf = TimeFrequency(freqs, sfreq=100)
tf.fit_transform(X, None)
# 3-D X
Xt = tf.transform(X)
assert_array_equal(Xt.shape, [n_epochs, n_chans, n_freqs, n_times])
# 2-D X
Xt = tf.transform(X[:, 0, :])
assert_array_equal(Xt.shape, [n_epochs, n_freqs, n_times])
# 3-D with decim
tf = TimeFrequency(freqs, sfreq=100, decim=2)
Xt = tf.transform(X)
assert_array_equal(Xt.shape, [n_epochs, n_chans, n_freqs, n_times // 2])
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