def test_plot_ica_scores():
"""Test plotting of ICA scores."""
import matplotlib.pyplot as plt
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_pca_components=3,
n_pca_components=3)
with warnings.catch_warnings(record=True): # bad proj
ica.fit(raw, picks=picks)
ica.labels_ = dict()
ica.labels_['eog/0/foo'] = 0
ica.labels_['eog'] = 0
ica.labels_['ecg'] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
assert_raises(ValueError,
ica.plot_scores, [0.3, 0.2],
axhline=[0.1, -0.1],
labels=['one', 'one-too-many'])
assert_raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')
def test_plot_ica_sources():
"""Test plotting of ICA panel."""
raw = read_raw_fif(raw_fname).crop(0, 1).load_data()
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
ecg=False,
eog=False,
exclude='bads')
ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=ica_picks)
ica.exclude = [1]
fig = ica.plot_sources(raw)
fig.canvas.key_press_event('escape')
# Sadly close_event isn't called on Agg backend and the test always passes.
assert_array_equal(ica.exclude, [1])
plt.close('all')
# dtype can change int->np.int after load, test it explicitly
ica.n_components_ = np.int64(ica.n_components_)
fig = ica.plot_sources(raw)
# also test mouse clicks
data_ax = fig.axes[0]
assert len(plt.get_fignums()) == 1
_fake_click(fig, data_ax, [-0.1, 0.9]) # click on y-label
assert len(plt.get_fignums()) == 2
ica.exclude = [1]
ica.plot_sources(raw)
raw.info['bads'] = ['MEG 0113']
with pytest.raises(RuntimeError, match="Raw doesn't match fitted data"):
ica.plot_sources(inst=raw)
ica.plot_sources(epochs)
epochs.info['bads'] = ['MEG 0113']
with pytest.raises(RuntimeError, match="Epochs don't match fitted data"):
ica.plot_sources(inst=epochs)
epochs.info['bads'] = []
ica.plot_sources(epochs.average())
evoked = epochs.average()
fig = ica.plot_sources(evoked)
# Test a click
ax = fig.get_axes()[0]
line = ax.lines[0]
_fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], 'data')
_fake_click(fig, ax, [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
# plot with bad channels excluded
ica.exclude = [0]
ica.plot_sources(evoked)
ica.labels_ = dict(eog=[0])
ica.labels_['eog/0/crazy-channel'] = [0]
ica.plot_sources(evoked) # now with labels
with pytest.raises(ValueError, match='must be of Raw or Epochs type'):
ica.plot_sources('meeow')
plt.close('all')
def test_plot_ica_sources():
"""Test plotting of ICA panel."""
import matplotlib.pyplot as plt
raw = read_raw_fif(raw_fname).crop(0, 1).load_data()
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
ecg=False,
eog=False,
exclude='bads')
ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=ica_picks)
ica.exclude = [1]
fig = ica.plot_sources(raw)
fig.canvas.key_press_event('escape')
# Sadly close_event isn't called on Agg backend and the test always passes.
assert_array_equal(ica.exclude, [1])
fig = ica.plot_sources(raw, [1])
# test mouse clicks
data_ax = fig.axes[0]
_fake_click(fig, data_ax, [-0.1, 0.9]) # click on y-label
raw.info['bads'] = ['MEG 0113']
assert_raises(RuntimeError, ica.plot_sources, inst=raw)
ica.plot_sources(epochs)
epochs.info['bads'] = ['MEG 0113']
assert_raises(RuntimeError, ica.plot_sources, inst=epochs)
epochs.info['bads'] = []
with warnings.catch_warnings(record=True): # no labeled objects mpl
ica.plot_sources(epochs.average())
evoked = epochs.average()
fig = ica.plot_sources(evoked)
# Test a click
ax = fig.get_axes()[0]
line = ax.lines[0]
_fake_click(
fig, ax,
[line.get_xdata()[0], line.get_ydata()[0]], 'data')
_fake_click(fig, ax, [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
# plot with bad channels excluded
ica.plot_sources(evoked, exclude=[0])
ica.exclude = [0]
ica.plot_sources(evoked) # does the same thing
ica.labels_ = dict(eog=[0])
ica.labels_['eog/0/crazy-channel'] = [0]
ica.plot_sources(evoked) # now with labels
assert_raises(ValueError, ica.plot_sources, 'meeow')
plt.close('all')
def test_plot_ica_sources():
"""Test plotting of ICA panel."""
raw = read_raw_fif(raw_fname).crop(0, 1).load_data()
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
ecg=False, eog=False, exclude='bads')
ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=ica_picks)
ica.exclude = [1]
fig = ica.plot_sources(raw)
fig.canvas.key_press_event('escape')
# Sadly close_event isn't called on Agg backend and the test always passes.
assert_array_equal(ica.exclude, [1])
plt.close('all')
# dtype can change int->np.int after load, test it explicitly
ica.n_components_ = np.int64(ica.n_components_)
fig = ica.plot_sources(raw, [1])
# also test mouse clicks
data_ax = fig.axes[0]
_fake_click(fig, data_ax, [-0.1, 0.9]) # click on y-label
raw.info['bads'] = ['MEG 0113']
pytest.raises(RuntimeError, ica.plot_sources, inst=raw)
ica.plot_sources(epochs)
epochs.info['bads'] = ['MEG 0113']
pytest.raises(RuntimeError, ica.plot_sources, inst=epochs)
epochs.info['bads'] = []
ica.plot_sources(epochs.average())
evoked = epochs.average()
fig = ica.plot_sources(evoked)
# Test a click
ax = fig.get_axes()[0]
line = ax.lines[0]
_fake_click(fig, ax,
[line.get_xdata()[0], line.get_ydata()[0]], 'data')
_fake_click(fig, ax,
[ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
# plot with bad channels excluded
ica.plot_sources(evoked, exclude=[0])
ica.exclude = [0]
ica.plot_sources(evoked) # does the same thing
ica.labels_ = dict(eog=[0])
ica.labels_['eog/0/crazy-channel'] = [0]
ica.plot_sources(evoked) # now with labels
pytest.raises(ValueError, ica.plot_sources, 'meeow')
plt.close('all')
def test_plot_ica_sources():
"""Test plotting of ICA panel
"""
import matplotlib.pyplot as plt
raw = io.Raw(raw_fname, preload=False)
raw.crop(0, 1, copy=False)
raw.load_data()
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
ecg=False,
eog=False,
exclude='bads')
ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=ica_picks)
raw.info['bads'] = ['MEG 0113']
assert_raises(RuntimeError, ica.plot_sources, inst=raw)
ica.plot_sources(epochs)
epochs.info['bads'] = ['MEG 0113']
assert_raises(RuntimeError, ica.plot_sources, inst=epochs)
epochs.info['bads'] = []
with warnings.catch_warnings(record=True): # no labeled objects mpl
ica.plot_sources(epochs.average())
evoked = epochs.average()
fig = ica.plot_sources(evoked)
# Test a click
ax = fig.get_axes()[0]
line = ax.lines[0]
_fake_click(
fig, ax,
[line.get_xdata()[0], line.get_ydata()[0]], 'data')
_fake_click(fig, ax, [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
# plot with bad channels excluded
ica.plot_sources(evoked, exclude=[0])
ica.exclude = [0]
ica.plot_sources(evoked) # does the same thing
ica.labels_ = dict(eog=[0])
ica.labels_['eog/0/crazy-channel'] = [0]
ica.plot_sources(evoked) # now with labels
assert_raises(ValueError, ica.plot_sources, 'meeow')
plt.close('all')
def test_plot_ica_sources():
"""Test plotting of ICA panel
"""
import matplotlib.pyplot as plt
raw = io.read_raw_fif(raw_fname,
preload=False).crop(0, 1, copy=False).load_data()
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
ecg=False, eog=False, exclude='bads')
ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=ica_picks)
ica.exclude = [1]
fig = ica.plot_sources(raw)
fig.canvas.key_press_event('escape')
# Sadly close_event isn't called on Agg backend and the test always passes.
assert_array_equal(ica.exclude, [1])
raw.info['bads'] = ['MEG 0113']
assert_raises(RuntimeError, ica.plot_sources, inst=raw)
ica.plot_sources(epochs)
epochs.info['bads'] = ['MEG 0113']
assert_raises(RuntimeError, ica.plot_sources, inst=epochs)
epochs.info['bads'] = []
with warnings.catch_warnings(record=True): # no labeled objects mpl
ica.plot_sources(epochs.average())
evoked = epochs.average()
fig = ica.plot_sources(evoked)
# Test a click
ax = fig.get_axes()[0]
line = ax.lines[0]
_fake_click(fig, ax,
[line.get_xdata()[0], line.get_ydata()[0]], 'data')
_fake_click(fig, ax,
[ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
# plot with bad channels excluded
ica.plot_sources(evoked, exclude=[0])
ica.exclude = [0]
ica.plot_sources(evoked) # does the same thing
ica.labels_ = dict(eog=[0])
ica.labels_['eog/0/crazy-channel'] = [0]
ica.plot_sources(evoked) # now with labels
assert_raises(ValueError, ica.plot_sources, 'meeow')
plt.close('all')
def test_plot_ica_scores():
"""Test plotting of ICA scores."""
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_pca_components=3,
n_pca_components=3)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw, picks=picks)
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], figsize=(6.4, 2.7))
ica.plot_scores([[0.3, 0.2], [0.3, 0.2]], axhline=[0.1, -0.1])
# check labels
ica.labels_ = dict()
ica.labels_['eog'] = 0
ica.labels_['ecg'] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
ica.labels_['eog/0/foo'] = 0
ica.labels_['ecg/1/bar'] = 0
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
# check setting number of columns
fig = ica.plot_scores([[0.3, 0.2], [0.3, 0.2], [0.3, 0.2]],
axhline=[0.1, -0.1])
assert 2 == fig.get_axes()[0].get_geometry()[1]
fig = ica.plot_scores([[0.3, 0.2], [0.3, 0.2]],
axhline=[0.1, -0.1],
n_cols=1)
assert 1 == fig.get_axes()[0].get_geometry()[1]
# only use 1 column (even though 2 were requested)
fig = ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], n_cols=2)
assert 1 == fig.get_axes()[0].get_geometry()[1]
pytest.raises(ValueError,
ica.plot_scores, [0.3, 0.2],
axhline=[0.1, -0.1],
labels=['one', 'one-too-many'])
pytest.raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')
def test_plot_ica_scores():
"""Test plotting of ICA scores."""
import matplotlib.pyplot as plt
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2, max_pca_components=3, n_pca_components=3)
with warnings.catch_warnings(record=True): # bad proj
ica.fit(raw, picks=picks)
ica.labels_ = dict()
ica.labels_["eog/0/foo"] = 0
ica.labels_["eog"] = 0
ica.labels_["ecg"] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels="foo")
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels="eog")
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels="ecg")
assert_raises(ValueError, ica.plot_scores, [0.3, 0.2], axhline=[0.1, -0.1], labels=["one", "one-too-many"])
assert_raises(ValueError, ica.plot_scores, [0.2])
plt.close("all")
def test_plot_ica_scores():
"""Test plotting of ICA scores."""
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw, picks=picks)
ica.labels_ = dict()
ica.labels_['eog/0/foo'] = 0
ica.labels_['eog'] = 0
ica.labels_['ecg'] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
pytest.raises(
ValueError,
ica.plot_scores,
[0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
pytest.raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')
def test_plot_ica_scores():
"""Test plotting of ICA scores."""
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw, picks=picks)
ica.labels_ = dict()
ica.labels_['eog/0/foo'] = 0
ica.labels_['eog'] = 0
ica.labels_['ecg'] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
pytest.raises(
ValueError,
ica.plot_scores,
[0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
pytest.raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')
def test_plot_ica_scores():
"""Test plotting of ICA scores
"""
import matplotlib.pyplot as plt
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=picks)
ica.labels_ = dict()
ica.labels_['eog/0/foo'] = 0
ica.labels_['eog'] = 0
ica.labels_['ecg'] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
assert_raises(
ValueError,
ica.plot_scores,
[0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
assert_raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')
def test_plot_ica_sources(raw_orig, mpl_backend):
"""Test plotting of ICA panel."""
raw = raw_orig.copy().crop(0, 1)
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
ecg=False,
eog=False,
exclude='bads')
ica = ICA(n_components=2)
ica.fit(raw, picks=ica_picks)
ica.exclude = [1]
fig = ica.plot_sources(raw)
assert mpl_backend._get_n_figs() == 1
# change which component is in ICA.exclude (click data trace to remove
# current one; click name to add other one)
fig._redraw()
# ToDo: This will be different methods in pyqtgraph
x = fig.mne.traces[1].get_xdata()[5]
y = fig.mne.traces[1].get_ydata()[5]
fig._fake_click((x, y), xform='data') # exclude = []
_click_ch_name(fig, ch_index=0, button=1) # exclude = [0]
fig._fake_keypress(fig.mne.close_key)
fig._close_event()
assert mpl_backend._get_n_figs() == 0
assert_array_equal(ica.exclude, [0])
# test when picks does not include ica.exclude.
fig = ica.plot_sources(raw, picks=[1])
assert len(plt.get_fignums()) == 1
mpl_backend._close_all()
# dtype can change int->np.int64 after load, test it explicitly
ica.n_components_ = np.int64(ica.n_components_)
# test clicks on y-label (need >2 secs for plot_properties() to work)
long_raw = raw_orig.crop(0, 5)
fig = ica.plot_sources(long_raw)
assert len(plt.get_fignums()) == 1
fig._redraw()
_click_ch_name(fig, ch_index=0, button=3)
assert len(fig.mne.child_figs) == 1
assert len(plt.get_fignums()) == 2
# close child fig directly (workaround for mpl issue #18609)
fig._fake_keypress('escape', fig=fig.mne.child_figs[0])
assert len(plt.get_fignums()) == 1
fig._fake_keypress(fig.mne.close_key)
assert len(plt.get_fignums()) == 0
del long_raw
# test with annotations
orig_annot = raw.annotations
raw.set_annotations(Annotations([0.2], [0.1], 'Test'))
fig = ica.plot_sources(raw)
assert len(fig.mne.ax_main.collections) == 1
assert len(fig.mne.ax_hscroll.collections) == 1
raw.set_annotations(orig_annot)
# test error handling
raw_ = raw.copy().load_data()
raw_.drop_channels('MEG 0113')
with pytest.raises(RuntimeError, match="Raw doesn't match fitted data"), \
pytest.warns(RuntimeWarning, match='could not be picked'):
ica.plot_sources(inst=raw_)
epochs_ = epochs.copy().load_data()
epochs_.drop_channels('MEG 0113')
with pytest.raises(RuntimeError, match="Epochs don't match fitted data"), \
pytest.warns(RuntimeWarning, match='could not be picked'):
ica.plot_sources(inst=epochs_)
del raw_
del epochs_
# test w/ epochs and evokeds
ica.plot_sources(epochs)
ica.plot_sources(epochs.average())
evoked = epochs.average()
fig = ica.plot_sources(evoked)
# Test a click
ax = fig.get_axes()[0]
line = ax.lines[0]
_fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], 'data')
_fake_click(fig, ax, [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
# plot with bad channels excluded
ica.exclude = [0]
ica.plot_sources(evoked)
# pretend find_bads_eog() yielded some results
ica.labels_ = {'eog': [0], 'eog/0/crazy-channel': [0]}
ica.plot_sources(evoked) # now with labels
# pass an invalid inst
with pytest.raises(ValueError, match='must be of Raw or Epochs type'):
ica.plot_sources('meeow')
def test_ica_additional(method):
"""Test additional ICA functionality."""
_skip_check_picard(method)
tempdir = _TempDir()
stop2 = 500
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
raw.del_proj() # avoid warnings
raw.set_annotations(Annotations([0.5], [0.5], ['BAD']))
# XXX This breaks the tests :(
# raw.info['bads'] = [raw.ch_names[1]]
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')[1::2]
epochs = Epochs(raw,
events,
None,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True,
proj=False)
epochs.decimate(3, verbose='error')
assert len(epochs) == 4
# test if n_components=None works
ica = ICA(n_components=None,
max_pca_components=None,
n_pca_components=None,
random_state=0,
method=method,
max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(epochs)
# for testing eog functionality
picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)])
epochs_eog = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks2,
baseline=(None, 0),
preload=True)
del picks2
test_cov2 = test_cov.copy()
ica = ICA(noise_cov=test_cov2,
n_components=3,
max_pca_components=4,
n_pca_components=4,
method=method)
assert (ica.info is None)
with pytest.warns(RuntimeWarning, match='normalize_proj'):
ica.fit(raw, picks[:5])
assert (isinstance(ica.info, Info))
assert (ica.n_components_ < 5)
ica = ICA(n_components=3,
max_pca_components=4,
method=method,
n_pca_components=4,
random_state=0)
pytest.raises(RuntimeError, ica.save, '')
ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)
# check passing a ch_name to find_bads_ecg
with pytest.warns(RuntimeWarning, match='longer'):
_, scores_1 = ica.find_bads_ecg(raw)
_, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1])
assert scores_1[0] != scores_2[0]
# test corrmap
ica2 = ica.copy()
ica3 = ica.copy()
corrmap([ica, ica2], (0, 0),
threshold='auto',
label='blinks',
plot=True,
ch_type="mag")
corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False)
assert (ica.labels_["blinks"] == ica2.labels_["blinks"])
assert (0 in ica.labels_["blinks"])
# test retrieval of component maps as arrays
components = ica.get_components()
template = components[:, 0]
EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s')
corrmap([ica, ica3],
template,
threshold='auto',
label='blinks',
plot=True,
ch_type="mag")
assert (ica2.labels_["blinks"] == ica3.labels_["blinks"])
plt.close('all')
# make sure a single threshold in a list works
corrmap([ica, ica3],
template,
threshold=[0.5],
label='blinks',
plot=True,
ch_type="mag")
ica_different_channels = ICA(n_components=2,
random_state=0).fit(raw, picks=[2, 3, 4, 5])
pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0))
# test warnings on bad filenames
ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
with pytest.warns(RuntimeWarning, match='-ica.fif'):
ica.save(ica_badname)
with pytest.warns(RuntimeWarning, match='-ica.fif'):
read_ica(ica_badname)
# test decim
ica = ICA(n_components=3,
max_pca_components=4,
n_pca_components=4,
method=method,
max_iter=1)
raw_ = raw.copy()
for _ in range(3):
raw_.append(raw_)
n_samples = raw_._data.shape[1]
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=picks[:5], decim=3)
assert raw_._data.shape[1] == n_samples
# test expl var
ica = ICA(n_components=1.0,
max_pca_components=4,
n_pca_components=4,
method=method,
max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=None, decim=3)
assert (ica.n_components_ == 4)
ica_var = _ica_explained_variance(ica, raw, normalize=True)
assert (np.all(ica_var[:-1] >= ica_var[1:]))
# test ica sorting
ica.exclude = [0]
ica.labels_ = dict(blink=[0], think=[1])
ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True)
assert_equal(ica_sorted.exclude, [3])
assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2]))
# epochs extraction from raw fit
pytest.raises(RuntimeError, ica.get_sources, epochs)
# test reading and writing
test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
for cov in (None, test_cov):
ica = ICA(noise_cov=cov,
n_components=2,
max_pca_components=4,
n_pca_components=4,
method=method,
max_iter=1)
with pytest.warns(None): # ICA does not converge
ica.fit(raw, picks=picks[:10], start=start, stop=stop2)
sources = ica.get_sources(epochs).get_data()
assert (ica.mixing_matrix_.shape == (2, 2))
assert (ica.unmixing_matrix_.shape == (2, 2))
assert (ica.pca_components_.shape == (4, 10))
assert (sources.shape[1] == ica.n_components_)
for exclude in [[], [0], np.array([1, 2, 3])]:
ica.exclude = exclude
ica.labels_ = {'foo': [0]}
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (list(ica.exclude) == ica_read.exclude)
assert_equal(ica.labels_, ica_read.labels_)
ica.apply(raw)
ica.exclude = []
ica.apply(raw, exclude=[1])
assert (ica.exclude == [])
ica.exclude = [0, 1]
ica.apply(raw, exclude=[1])
assert (ica.exclude == [0, 1])
ica_raw = ica.get_sources(raw)
assert (ica.exclude == [
ica_raw.ch_names.index(e) for e in ica_raw.info['bads']
])
# test filtering
d1 = ica_raw._data[0].copy()
ica_raw.filter(4, 20, fir_design='firwin2')
assert_equal(ica_raw.info['lowpass'], 20.)
assert_equal(ica_raw.info['highpass'], 4.)
assert ((d1 != ica_raw._data[0]).any())
d1 = ica_raw._data[0].copy()
ica_raw.notch_filter([10], trans_bandwidth=10, fir_design='firwin')
assert ((d1 != ica_raw._data[0]).any())
ica.n_pca_components = 2
ica.method = 'fake'
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (ica.n_pca_components == ica_read.n_pca_components)
assert_equal(ica.method, ica_read.method)
assert_equal(ica.labels_, ica_read.labels_)
# check type consistency
attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ '
'pca_explained_variance_ pre_whitener_')
def f(x, y):
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in [
'mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_', 'pre_whitener_'
]:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert (ica.ch_names == ica_read.ch_names)
assert (isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw,
target='EOG 061',
score_func=func,
start=0,
stop=10)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, start=0, stop=50, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, raw, target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw,
start_find=0,
stop_find=50,
ecg_ch=ch_name,
eog_ch=ch_name,
skew_criterion=idx,
var_criterion=idx,
kurt_criterion=idx)
# Make sure detect_artifacts marks the right components.
# For int criterion, the doc says "E.g. range(2) would return the two
# sources with the highest score". Assert that's what it does.
# Only test for skew, since it's always the same code.
ica.exclude = []
ica.detect_artifacts(raw,
start_find=0,
stop_find=50,
ecg_ch=None,
eog_ch=None,
skew_criterion=0,
var_criterion=None,
kurt_criterion=None)
assert np.abs(scores[ica.exclude]) == np.max(np.abs(scores))
evoked = epochs.average()
evoked_data = evoked.data.copy()
raw_data = raw[:][0].copy()
epochs_data = epochs.get_data().copy()
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
pytest.raises(ValueError,
ica.find_bads_ecg,
epochs.average(),
method='ctps')
pytest.raises(ValueError, ica.find_bads_ecg, raw, method='crazy-coupling')
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert (isinstance(scores, list))
assert_equal(len(scores[0]), ica.n_components_)
idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(evoked, method='correlation')
assert_equal(len(scores), ica.n_components_)
assert_array_equal(raw_data, raw[:][0])
assert_array_equal(epochs_data, epochs.get_data())
assert_array_equal(evoked_data, evoked.data)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(epochs_eog,
target='EOG 061',
score_func=func)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(epochs, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, epochs, target=np.arange(1))
# ecg functionality
ecg_scores = ica.score_sources(raw,
target='MEG 1531',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
ecg_events = ica_find_ecg_events(raw,
sources[np.abs(ecg_scores).argmax()])
assert (ecg_events.ndim == 2)
# eog functionality
eog_scores = ica.score_sources(raw,
target='EOG 061',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
eog_events = ica_find_eog_events(raw,
sources[np.abs(eog_scores).argmax()])
assert (eog_events.ndim == 2)
# Test ica fiff export
ica_raw = ica.get_sources(raw, start=0, stop=100)
assert (ica_raw.last_samp - ica_raw.first_samp == 100)
assert_equal(len(ica_raw._filenames), 1) # API consistency
ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif')
ica.n_components = np.int32(ica.n_components)
ica_raw.save(test_ica_fname, overwrite=True)
ica_raw2 = read_raw_fif(test_ica_fname, preload=True)
assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4)
ica_raw2.close()
os.remove(test_ica_fname)
# Test ica epochs export
ica_epochs = ica.get_sources(epochs)
assert (ica_epochs.events.shape == epochs.events.shape)
ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
assert (ica.n_components_ == ica_epochs.get_data().shape[1])
assert (ica_epochs._raw is None)
assert (ica_epochs.preload is True)
# test float n pca components
ica.pca_explained_variance_ = np.array([0.2] * 5)
ica.n_components_ = 0
for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]:
ncomps_ = ica._check_n_pca_components(ncomps)
assert (ncomps_ == expected)
ica = ICA(method=method)
with pytest.warns(None): # sometimes does not converge
ica.fit(raw, picks=picks[:5])
with pytest.warns(RuntimeWarning, match='longer'):
ica.find_bads_ecg(raw)
ica.find_bads_eog(epochs, ch_name='MEG 0121')
assert_array_equal(raw_data, raw[:][0])
raw.drop_channels(['MEG 0122'])
pytest.raises(RuntimeError, ica.find_bads_eog, raw)
with pytest.warns(RuntimeWarning, match='longer'):
pytest.raises(RuntimeError, ica.find_bads_ecg, raw)
def test_ica_additional(method):
"""Test additional ICA functionality."""
_skip_check_picard(method)
tempdir = _TempDir()
stop2 = 500
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
raw.del_proj() # avoid warnings
raw.set_annotations(Annotations([0.5], [0.5], ['BAD']))
# XXX This breaks the tests :(
# raw.info['bads'] = [raw.ch_names[1]]
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[1::2]
epochs = Epochs(raw, events, None, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, proj=False)
epochs.decimate(3, verbose='error')
assert len(epochs) == 4
# test if n_components=None works
ica = ICA(n_components=None, max_pca_components=None,
n_pca_components=None, random_state=0, method=method, max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(epochs)
# for testing eog functionality
picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)])
epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2,
baseline=(None, 0), preload=True)
del picks2
test_cov2 = test_cov.copy()
ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
n_pca_components=4, method=method)
assert (ica.info is None)
with pytest.warns(RuntimeWarning, match='normalize_proj'):
ica.fit(raw, picks[:5])
assert (isinstance(ica.info, Info))
assert (ica.n_components_ < 5)
ica = ICA(n_components=3, max_pca_components=4, method=method,
n_pca_components=4, random_state=0)
pytest.raises(RuntimeError, ica.save, '')
ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)
# check passing a ch_name to find_bads_ecg
with pytest.warns(RuntimeWarning, match='longer'):
_, scores_1 = ica.find_bads_ecg(raw)
_, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1])
assert scores_1[0] != scores_2[0]
# test corrmap
ica2 = ica.copy()
ica3 = ica.copy()
corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True,
ch_type="mag")
corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False)
assert (ica.labels_["blinks"] == ica2.labels_["blinks"])
assert (0 in ica.labels_["blinks"])
# test retrieval of component maps as arrays
components = ica.get_components()
template = components[:, 0]
EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s')
corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True,
ch_type="mag")
assert (ica2.labels_["blinks"] == ica3.labels_["blinks"])
plt.close('all')
ica_different_channels = ICA(n_components=2, random_state=0).fit(
raw, picks=[2, 3, 4, 5])
pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0))
# test warnings on bad filenames
ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
with pytest.warns(RuntimeWarning, match='-ica.fif'):
ica.save(ica_badname)
with pytest.warns(RuntimeWarning, match='-ica.fif'):
read_ica(ica_badname)
# test decim
ica = ICA(n_components=3, max_pca_components=4,
n_pca_components=4, method=method, max_iter=1)
raw_ = raw.copy()
for _ in range(3):
raw_.append(raw_)
n_samples = raw_._data.shape[1]
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=picks[:5], decim=3)
assert raw_._data.shape[1] == n_samples
# test expl var
ica = ICA(n_components=1.0, max_pca_components=4,
n_pca_components=4, method=method, max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=None, decim=3)
assert (ica.n_components_ == 4)
ica_var = _ica_explained_variance(ica, raw, normalize=True)
assert (np.all(ica_var[:-1] >= ica_var[1:]))
# test ica sorting
ica.exclude = [0]
ica.labels_ = dict(blink=[0], think=[1])
ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True)
assert_equal(ica_sorted.exclude, [3])
assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2]))
# epochs extraction from raw fit
pytest.raises(RuntimeError, ica.get_sources, epochs)
# test reading and writing
test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
for cov in (None, test_cov):
ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4,
n_pca_components=4, method=method, max_iter=1)
with pytest.warns(None): # ICA does not converge
ica.fit(raw, picks=picks[:10], start=start, stop=stop2)
sources = ica.get_sources(epochs).get_data()
assert (ica.mixing_matrix_.shape == (2, 2))
assert (ica.unmixing_matrix_.shape == (2, 2))
assert (ica.pca_components_.shape == (4, 10))
assert (sources.shape[1] == ica.n_components_)
for exclude in [[], [0], np.array([1, 2, 3])]:
ica.exclude = exclude
ica.labels_ = {'foo': [0]}
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (list(ica.exclude) == ica_read.exclude)
assert_equal(ica.labels_, ica_read.labels_)
ica.apply(raw)
ica.exclude = []
ica.apply(raw, exclude=[1])
assert (ica.exclude == [])
ica.exclude = [0, 1]
ica.apply(raw, exclude=[1])
assert (ica.exclude == [0, 1])
ica_raw = ica.get_sources(raw)
assert (ica.exclude == [ica_raw.ch_names.index(e) for e in
ica_raw.info['bads']])
# test filtering
d1 = ica_raw._data[0].copy()
ica_raw.filter(4, 20, fir_design='firwin2')
assert_equal(ica_raw.info['lowpass'], 20.)
assert_equal(ica_raw.info['highpass'], 4.)
assert ((d1 != ica_raw._data[0]).any())
d1 = ica_raw._data[0].copy()
ica_raw.notch_filter([10], trans_bandwidth=10, fir_design='firwin')
assert ((d1 != ica_raw._data[0]).any())
ica.n_pca_components = 2
ica.method = 'fake'
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (ica.n_pca_components == ica_read.n_pca_components)
assert_equal(ica.method, ica_read.method)
assert_equal(ica.labels_, ica_read.labels_)
# check type consistency
attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ '
'pca_explained_variance_ pre_whitener_')
def f(x, y):
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_',
'pre_whitener_']:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert (ica.ch_names == ica_read.ch_names)
assert (isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw, target='EOG 061', score_func=func,
start=0, stop=10)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, start=0, stop=50, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, raw,
target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name,
eog_ch=ch_name, skew_criterion=idx,
var_criterion=idx, kurt_criterion=idx)
# Make sure detect_artifacts marks the right components.
# For int criterion, the doc says "E.g. range(2) would return the two
# sources with the highest score". Assert that's what it does.
# Only test for skew, since it's always the same code.
ica.exclude = []
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=None,
eog_ch=None, skew_criterion=0,
var_criterion=None, kurt_criterion=None)
assert np.abs(scores[ica.exclude]) == np.max(np.abs(scores))
evoked = epochs.average()
evoked_data = evoked.data.copy()
raw_data = raw[:][0].copy()
epochs_data = epochs.get_data().copy()
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
pytest.raises(ValueError, ica.find_bads_ecg, epochs.average(),
method='ctps')
pytest.raises(ValueError, ica.find_bads_ecg, raw,
method='crazy-coupling')
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert (isinstance(scores, list))
assert_equal(len(scores[0]), ica.n_components_)
idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(evoked, method='correlation')
assert_equal(len(scores), ica.n_components_)
assert_array_equal(raw_data, raw[:][0])
assert_array_equal(epochs_data, epochs.get_data())
assert_array_equal(evoked_data, evoked.data)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(epochs_eog, target='EOG 061',
score_func=func)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(epochs, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, epochs,
target=np.arange(1))
# ecg functionality
ecg_scores = ica.score_sources(raw, target='MEG 1531',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
ecg_events = ica_find_ecg_events(
raw, sources[np.abs(ecg_scores).argmax()])
assert (ecg_events.ndim == 2)
# eog functionality
eog_scores = ica.score_sources(raw, target='EOG 061',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
eog_events = ica_find_eog_events(
raw, sources[np.abs(eog_scores).argmax()])
assert (eog_events.ndim == 2)
# Test ica fiff export
ica_raw = ica.get_sources(raw, start=0, stop=100)
assert (ica_raw.last_samp - ica_raw.first_samp == 100)
assert_equal(len(ica_raw._filenames), 1) # API consistency
ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif')
ica.n_components = np.int32(ica.n_components)
ica_raw.save(test_ica_fname, overwrite=True)
ica_raw2 = read_raw_fif(test_ica_fname, preload=True)
assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4)
ica_raw2.close()
os.remove(test_ica_fname)
# Test ica epochs export
ica_epochs = ica.get_sources(epochs)
assert (ica_epochs.events.shape == epochs.events.shape)
ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
assert (ica.n_components_ == ica_epochs.get_data().shape[1])
assert (ica_epochs._raw is None)
assert (ica_epochs.preload is True)
# test float n pca components
ica.pca_explained_variance_ = np.array([0.2] * 5)
ica.n_components_ = 0
for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]:
ncomps_ = ica._check_n_pca_components(ncomps)
assert (ncomps_ == expected)
ica = ICA(method=method)
with pytest.warns(None): # sometimes does not converge
ica.fit(raw, picks=picks[:5])
with pytest.warns(RuntimeWarning, match='longer'):
ica.find_bads_ecg(raw)
ica.find_bads_eog(epochs, ch_name='MEG 0121')
assert_array_equal(raw_data, raw[:][0])
raw.drop_channels(['MEG 0122'])
pytest.raises(RuntimeError, ica.find_bads_eog, raw)
with pytest.warns(RuntimeWarning, match='longer'):
pytest.raises(RuntimeError, ica.find_bads_ecg, raw)
def test_plot_ica_sources():
"""Test plotting of ICA panel."""
raw = read_raw_fif(raw_fname).crop(0, 1).load_data()
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
ecg=False,
eog=False,
exclude='bads')
ica = ICA(n_components=2)
ica.fit(raw, picks=ica_picks)
ica.exclude = [1]
fig = ica.plot_sources(raw)
assert len(plt.get_fignums()) == 1
# change which component is in ICA.exclude (click data trace to remove
# current one; click name to add other one)
fig.canvas.draw()
x = fig.mne.traces[1].get_xdata()[5]
y = fig.mne.traces[1].get_ydata()[5]
_fake_click(fig, fig.mne.ax_main, (x, y), xform='data') # exclude = []
_click_ch_name(fig, ch_index=0, button=1) # exclude = [0]
fig.canvas.key_press_event(fig.mne.close_key)
_close_event(fig)
assert len(plt.get_fignums()) == 0
assert_array_equal(ica.exclude, [0])
# test when picks does not include ica.exclude.
fig = ica.plot_sources(raw, picks=[1])
assert len(plt.get_fignums()) == 1
plt.close('all')
# dtype can change int->np.int64 after load, test it explicitly
ica.n_components_ = np.int64(ica.n_components_)
# test clicks on y-label (need >2 secs for plot_properties() to work)
long_raw = read_raw_fif(raw_fname).crop(0, 5).load_data()
fig = ica.plot_sources(long_raw)
assert len(plt.get_fignums()) == 1
fig.canvas.draw()
_fake_click(fig, fig.mne.ax_main, (-0.1, 0), xform='data', button=3)
assert len(fig.mne.child_figs) == 1
assert len(plt.get_fignums()) == 2
# close child fig directly (workaround for mpl issue #18609)
fig.mne.child_figs[0].canvas.key_press_event('escape')
assert len(plt.get_fignums()) == 1
fig.canvas.key_press_event(fig.mne.close_key)
assert len(plt.get_fignums()) == 0
del long_raw
# test with annotations
orig_annot = raw.annotations
raw.set_annotations(Annotations([0.2], [0.1], 'Test'))
fig = ica.plot_sources(raw)
assert len(fig.mne.ax_main.collections) == 1
assert len(fig.mne.ax_hscroll.collections) == 1
raw.set_annotations(orig_annot)
# test error handling
raw.info['bads'] = ['MEG 0113']
with pytest.raises(RuntimeError, match="Raw doesn't match fitted data"):
ica.plot_sources(inst=raw)
epochs.info['bads'] = ['MEG 0113']
with pytest.raises(RuntimeError, match="Epochs don't match fitted data"):
ica.plot_sources(inst=epochs)
epochs.info['bads'] = []
# test w/ epochs and evokeds
ica.plot_sources(epochs)
ica.plot_sources(epochs.average())
evoked = epochs.average()
fig = ica.plot_sources(evoked)
# Test a click
ax = fig.get_axes()[0]
line = ax.lines[0]
_fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], 'data')
_fake_click(fig, ax, [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
# plot with bad channels excluded
ica.exclude = [0]
ica.plot_sources(evoked)
ica.labels_ = dict(eog=[0])
ica.labels_['eog/0/crazy-channel'] = [0]
ica.plot_sources(evoked) # now with labels
with pytest.raises(ValueError, match='must be of Raw or Epochs type'):
ica.plot_sources('meeow')
def test_ica_additional():
"""Test additional ICA functionality"""
tempdir = _TempDir()
stop2 = 500
raw = Raw(raw_fname).crop(1.5, stop, False)
raw.load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
# test if n_components=None works
with warnings.catch_warnings(record=True):
ica = ICA(n_components=None,
max_pca_components=None,
n_pca_components=None, random_state=0)
ica.fit(epochs, picks=picks, decim=3)
# for testing eog functionality
picks2 = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=True, exclude='bads')
epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2,
baseline=(None, 0), preload=True)
test_cov2 = test_cov.copy()
ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
n_pca_components=4)
assert_true(ica.info is None)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks[:5])
assert_true(isinstance(ica.info, Info))
assert_true(ica.n_components_ < 5)
ica = ICA(n_components=3, max_pca_components=4,
n_pca_components=4)
assert_raises(RuntimeError, ica.save, '')
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)
# test corrmap
ica2 = ica.copy()
corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True,
ch_type="mag")
corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False)
assert_true(ica.labels_["blinks"] == ica2.labels_["blinks"])
assert_true(0 in ica.labels_["blinks"])
plt.close('all')
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
ica.save(ica_badname)
read_ica(ica_badname)
assert_naming(w, 'test_ica.py', 2)
# test decim
ica = ICA(n_components=3, max_pca_components=4,
n_pca_components=4)
raw_ = raw.copy()
for _ in range(3):
raw_.append(raw_)
n_samples = raw_._data.shape[1]
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=None, decim=3)
assert_true(raw_._data.shape[1], n_samples)
# test expl var
ica = ICA(n_components=1.0, max_pca_components=4,
n_pca_components=4)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=None, decim=3)
assert_true(ica.n_components_ == 4)
# epochs extraction from raw fit
assert_raises(RuntimeError, ica.get_sources, epochs)
# test reading and writing
test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
for cov in (None, test_cov):
ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4,
n_pca_components=4)
with warnings.catch_warnings(record=True): # ICA does not converge
ica.fit(raw, picks=picks, start=start, stop=stop2)
sources = ica.get_sources(epochs).get_data()
assert_true(ica.mixing_matrix_.shape == (2, 2))
assert_true(ica.unmixing_matrix_.shape == (2, 2))
assert_true(ica.pca_components_.shape == (4, len(picks)))
assert_true(sources.shape[1] == ica.n_components_)
for exclude in [[], [0]]:
ica.exclude = [0]
ica.labels_ = {'foo': [0]}
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert_true(ica.exclude == ica_read.exclude)
assert_equal(ica.labels_, ica_read.labels_)
ica.exclude = []
ica.apply(raw, exclude=[1])
assert_true(ica.exclude == [])
ica.exclude = [0, 1]
ica.apply(raw, exclude=[1])
assert_true(ica.exclude == [0, 1])
ica_raw = ica.get_sources(raw)
assert_true(ica.exclude == [ica_raw.ch_names.index(e) for e in
ica_raw.info['bads']])
# test filtering
d1 = ica_raw._data[0].copy()
with warnings.catch_warnings(record=True): # dB warning
ica_raw.filter(4, 20)
assert_true((d1 != ica_raw._data[0]).any())
d1 = ica_raw._data[0].copy()
with warnings.catch_warnings(record=True): # dB warning
ica_raw.notch_filter([10])
assert_true((d1 != ica_raw._data[0]).any())
ica.n_pca_components = 2
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert_true(ica.n_pca_components == ica_read.n_pca_components)
# check type consistency
attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ '
'pca_explained_variance_ _pre_whitener')
def f(x, y):
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_',
'_pre_whitener']:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert_true(ica.ch_names == ica_read.ch_names)
assert_true(isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check scrore funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw, target='EOG 061', score_func=func,
start=0, stop=10)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, raw,
target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # varicance, kurtosis idx params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name,
eog_ch=ch_name, skew_criterion=idx,
var_criterion=idx, kurt_criterion=idx)
with warnings.catch_warnings(record=True):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
assert_raises(ValueError, ica.find_bads_ecg, epochs.average(),
method='ctps')
assert_raises(ValueError, ica.find_bads_ecg, raw,
method='crazy-coupling')
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
idx, scores = ica.find_bads_eog(raw)
assert_true(isinstance(scores, list))
assert_equal(len(scores[0]), ica.n_components_)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(epochs_eog, target='EOG 061',
score_func=func)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(epochs, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, epochs,
target=np.arange(1))
# ecg functionality
ecg_scores = ica.score_sources(raw, target='MEG 1531',
score_func='pearsonr')
with warnings.catch_warnings(record=True): # filter attenuation warning
ecg_events = ica_find_ecg_events(raw,
sources[np.abs(ecg_scores).argmax()])
assert_true(ecg_events.ndim == 2)
# eog functionality
eog_scores = ica.score_sources(raw, target='EOG 061',
score_func='pearsonr')
with warnings.catch_warnings(record=True): # filter attenuation warning
eog_events = ica_find_eog_events(raw,
sources[np.abs(eog_scores).argmax()])
assert_true(eog_events.ndim == 2)
# Test ica fiff export
ica_raw = ica.get_sources(raw, start=0, stop=100)
assert_true(ica_raw.last_samp - ica_raw.first_samp == 100)
assert_true(len(ica_raw._filenames) == 0) # API consistency
ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch]
assert_true(ica.n_components_ == len(ica_chans))
test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif')
ica.n_components = np.int32(ica.n_components)
ica_raw.save(test_ica_fname, overwrite=True)
ica_raw2 = Raw(test_ica_fname, preload=True)
assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4)
ica_raw2.close()
os.remove(test_ica_fname)
# Test ica epochs export
ica_epochs = ica.get_sources(epochs)
assert_true(ica_epochs.events.shape == epochs.events.shape)
ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch]
assert_true(ica.n_components_ == len(ica_chans))
assert_true(ica.n_components_ == ica_epochs.get_data().shape[1])
assert_true(ica_epochs._raw is None)
assert_true(ica_epochs.preload is True)
# test float n pca components
ica.pca_explained_variance_ = np.array([0.2] * 5)
ica.n_components_ = 0
for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]:
ncomps_ = ica._check_n_pca_components(ncomps)
assert_true(ncomps_ == expected)
eog_inds, scores = ica.find_bads_eog(eog_epochs) # find via correlation
##############################################################################################################################
# ICA plots
ica.plot_scores(scores, exclude=eog_inds) # look at r scores of components
ica.plot_sources(eog_average, exclude=eog_inds) # look at source time course
ica.plot_components() #look at the component topography
# choose ICA component from previous pics
selected_ICA = call_msg_box()
eog_inds = [int(i) for i in selected_ICA.split(',')]
ica.labels_ = {'eog/0/AF7': eog_inds, 'eog/1/AF3': eog_inds, 'eog': eog_inds}
ica.plot_properties(
eog_epochs,
picks=eog_inds,
psd_args={'fmax': 35.},
image_args={
'sigma': 1.
}) # look at the combination of all infos for selected component
print(ica.labels_)
# confirm ICA component from plot_properties figure
selected_ICA = call_msg_box()
eog_inds = [int(i) for i in selected_ICA.split(',')]
