def test_xdawn_apply_transform():
"""Test Xdawn apply and transform."""
# get data
raw, events, picks = _get_data()
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
preload=True, baseline=None, verbose=False)
n_components = 2
# Fit Xdawn
xd = Xdawn(n_components=n_components, correct_overlap='auto')
xd.fit(epochs)
# apply on raw
xd.apply(raw)
# apply on epochs
xd.apply(epochs)
# apply on evoked
xd.apply(epochs.average())
# apply on other thing should raise an error
assert_raises(ValueError, xd.apply, 42)
# transform on epochs
xd.transform(epochs)
# transform on ndarray
xd.transform(epochs._data)
# transform on someting else
assert_raises(ValueError, xd.transform, 42)
def test_xdawn_apply_transform():
"""Test Xdawn apply and transform."""
# get data
raw, events, picks = _get_data()
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
preload=True,
baseline=None,
verbose=False)
n_components = 2
# Fit Xdawn
xd = Xdawn(n_components=n_components, correct_overlap='auto')
xd.fit(epochs)
# apply on raw
xd.apply(raw)
# apply on epochs
xd.apply(epochs)
# apply on evoked
xd.apply(epochs.average())
# apply on other thing should raise an error
assert_raises(ValueError, xd.apply, 42)
# transform on epochs
xd.transform(epochs)
# transform on ndarray
xd.transform(epochs._data)
# transform on someting else
assert_raises(ValueError, xd.transform, 42)
def test_xdawn_apply_transform():
"""Test Xdawn apply and transform."""
# Get data
raw, events, picks = _get_data()
raw.pick_types(eeg=True, meg=False)
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
preload=True, baseline=None,
verbose=False)
n_components = 2
# Fit Xdawn
xd = Xdawn(n_components=n_components, correct_overlap=False)
xd.fit(epochs)
# Apply on different types of instances
for inst in [raw, epochs.average(), epochs]:
denoise = xd.apply(inst)
# Apply on other thing should raise an error
assert_raises(ValueError, xd.apply, 42)
# Transform on epochs
xd.transform(epochs)
# Transform on ndarray
xd.transform(epochs._data)
# Transform on someting else
assert_raises(ValueError, xd.transform, 42)
# Check numerical results with shuffled epochs
np.random.seed(0) # random makes unstable linalg
idx = np.arange(len(epochs))
np.random.shuffle(idx)
xd.fit(epochs[idx])
denoise_shfl = xd.apply(epochs)
assert_array_almost_equal(denoise['cond2']._data,
denoise_shfl['cond2']._data)
def test_xdawn_apply_transform():
"""Test Xdawn apply and transform."""
# get data
raw, events, picks = _get_data()
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
preload=True, baseline=None, verbose=False)
n_components = 2
# Fit Xdawn
xd = Xdawn(n_components=n_components, correct_overlap='auto')
xd.fit(epochs)
# apply on raw
xd.apply(raw)
# apply on epochs
denoise = xd.apply(epochs)
# apply on evoked
xd.apply(epochs.average())
# apply on other thing should raise an error
assert_raises(ValueError, xd.apply, 42)
# transform on epochs
xd.transform(epochs)
# transform on ndarray
xd.transform(epochs._data)
# transform on someting else
assert_raises(ValueError, xd.transform, 42)
# check numerical results with shuffled epochs
idx = np.arange(len(epochs))
np.random.shuffle(idx)
xd.fit(epochs[idx])
denoise_shfl = xd.apply(epochs)
assert_array_equal(denoise['cond2']._data, denoise_shfl['cond2']._data)
def test_xdawn_apply_transform():
"""Test Xdawn apply and transform."""
# Get data
raw, events, picks = _get_data()
raw.pick_types(eeg=True, meg=False)
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=False,
add_eeg_ref=False,
preload=True,
baseline=None,
verbose=False)
n_components = 2
# Fit Xdawn
xd = Xdawn(n_components=n_components, correct_overlap=False)
xd.fit(epochs)
# Apply on different types of instances
for inst in [raw, epochs.average(), epochs]:
denoise = xd.apply(inst)
# Apply on other thing should raise an error
assert_raises(ValueError, xd.apply, 42)
# Transform on epochs
xd.transform(epochs)
# Transform on ndarray
xd.transform(epochs._data)
# Transform on someting else
assert_raises(ValueError, xd.transform, 42)
# Check numerical results with shuffled epochs
np.random.seed(0) # random makes unstable linalg
idx = np.arange(len(epochs))
np.random.shuffle(idx)
xd.fit(epochs[idx])
denoise_shfl = xd.apply(epochs)
assert_array_almost_equal(denoise['cond2']._data,
denoise_shfl['cond2']._data)
