def test_temporal_filter():
"""Test methods of TemporalFilter."""
X = np.random.rand(5, 5, 1200)
# Test init test
values = (('10hz', None, 100., 'auto'), (5., '10hz', 100., 'auto'),
(10., 20., 5., 'auto'), (None, None, 100., '5hz'))
for low, high, sf, ltrans in values:
filt = TemporalFilter(low, high, sf, ltrans, fir_design='firwin')
assert_raises(ValueError, filt.fit_transform, X)
# Add tests for different combinations of l_freq and h_freq
for low, high in ((5., 15.), (None, 15.), (5., None)):
filt = TemporalFilter(low, high, sfreq=100., fir_design='firwin')
Xt = filt.fit_transform(X)
assert_array_equal(filt.fit_transform(X), Xt)
assert_true(X.shape == Xt.shape)
# Test fit and transform numpy type check
with warnings.catch_warnings(record=True):
assert_raises(TypeError, filt.transform, [1, 2])
# Test with 2 dimensional data array
X = np.random.rand(101, 500)
filt = TemporalFilter(l_freq=25., h_freq=50., sfreq=1000.,
filter_length=150, fir_design='firwin2')
assert_equal(filt.fit_transform(X).shape, X.shape)
def test_temporal_filter():
"""Test methods of TemporalFilter."""
X = np.random.rand(5, 5, 1200)
# Test init test
values = (('10hz', None, 100., 'auto'), (5., '10hz', 100., 'auto'),
(10., 20., 5., 'auto'), (None, None, 100., '5hz'))
for low, high, sf, ltrans in values:
filt = TemporalFilter(low, high, sf, ltrans)
assert_raises(ValueError, filt.fit_transform, X)
# Add tests for different combinations of l_freq and h_freq
for low, high in ((5., 15.), (None, 15.), (5., None)):
filt = TemporalFilter(low, high, sfreq=100.)
Xt = filt.fit_transform(X)
assert_array_equal(filt.fit_transform(X), Xt)
assert_true(X.shape == Xt.shape)
# Test fit and transform numpy type check
with warnings.catch_warnings(record=True):
assert_raises(TypeError, filt.transform, [1, 2])
# Test with 2 dimensional data array
X = np.random.rand(101, 500)
filt = TemporalFilter(l_freq=25., h_freq=50., sfreq=1000.,
filter_length=150)
assert_equal(filt.fit_transform(X).shape, X.shape)
def test_temporal_filter():
"""Test methods of TemporalFilter."""
X = np.random.rand(5, 5, 1200)
# Test init test
values = (('10hz', None, 100., 'auto'), (5., '10hz', 100., 'auto'),
(10., 20., 5., 'auto'), (None, None, 100., '5hz'))
for low, high, sf, ltrans in values:
filt = TemporalFilter(low, high, sf, ltrans, fir_design='firwin')
pytest.raises(ValueError, filt.fit_transform, X)
# Add tests for different combinations of l_freq and h_freq
for low, high in ((5., 15.), (None, 15.), (5., None)):
filt = TemporalFilter(low, high, sfreq=100., fir_design='firwin')
Xt = filt.fit_transform(X)
assert_array_equal(filt.fit_transform(X), Xt)
assert (X.shape == Xt.shape)
# Test fit and transform numpy type check
with pytest.warns(RuntimeWarning, match='longer than the signal'):
pytest.raises(TypeError, filt.transform, [1, 2])
# Test with 2 dimensional data array
X = np.random.rand(101, 500)
filt = TemporalFilter(l_freq=25., h_freq=50., sfreq=1000.,
filter_length=150, fir_design='firwin2')
assert_equal(filt.fit_transform(X).shape, X.shape)
