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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
import pytest
from numpy.testing import assert_array_equal
pytest.importorskip("sklearn")
from sklearn.base import clone
from mne.decoding.time_frequency import TimeFrequency
def test_timefrequency():
"""Test TimeFrequency."""
# Init
n_freqs = 3
freqs = [20, 21, 22]
tf = TimeFrequency(freqs, sfreq=100)
for output in ["avg_power", "foo", None]:
pytest.raises(ValueError, TimeFrequency, freqs, output=output)
tf = clone(tf)
# Clone estimator
freqs_array = np.array(np.asarray(freqs))
tf = TimeFrequency(freqs_array, 100, "morlet", freqs_array / 5.0)
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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