def _create_ica_properties_fig(self, idx):
"""Show ICA properties for the selected component."""
from mne.viz.ica import (_create_properties_layout,
_prepare_data_ica_properties,
_fast_plot_ica_properties)
ch_name = self.mne.ch_names[idx]
if ch_name not in self.mne.ica._ica_names: # for EOG chans: do nothing
return
pick = self.mne.ica._ica_names.index(ch_name)
title = f'{ch_name} properties'
fig = self._new_child_figure(figsize=(7, 6),
fig_name=None,
window_title=title)
fig.suptitle(title)
fig, axes = _create_properties_layout(fig=fig)
if not hasattr(self.mne, 'data_ica_properties'):
# Precompute epoch sources only once
self.mne.data_ica_properties = _prepare_data_ica_properties(
self.mne.ica_inst, self.mne.ica)
_fast_plot_ica_properties(
self.mne.ica,
self.mne.ica_inst,
picks=pick,
axes=axes,
precomputed_data=self.mne.data_ica_properties,
show=False)
return fig
def test_plot_ica_properties():
"""Test plotting of ICA properties."""
import matplotlib.pyplot as plt
res = 8
raw = _get_raw(preload=True)
raw.add_proj([], remove_existing=True)
events = _get_events()
picks = _get_picks(raw)[:6]
pick_names = [raw.ch_names[k] for k in picks]
raw.pick_channels(pick_names)
with warnings.catch_warnings(record=True): # bad proj
epochs = Epochs(raw, events[:10], event_id, tmin, tmax,
baseline=(None, 0), preload=True)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=2, n_pca_components=2)
with warnings.catch_warnings(record=True): # bad proj
ica.fit(raw)
# test _create_properties_layout
fig, ax = _create_properties_layout()
assert_equal(len(ax), 5)
topoargs = dict(topomap_args={'res': res, 'contours': 0, "sensors": False})
ica.plot_properties(raw, picks=0, **topoargs)
ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
ica.plot_properties(epochs, picks=1, image_args={'sigma': 1.5},
topomap_args={'res': 10, 'colorbar': True},
psd_args={'fmax': 65.}, plot_std=False,
figsize=[4.5, 4.5])
plt.close('all')
assert_raises(ValueError, ica.plot_properties, epochs, dB=list('abc'))
assert_raises(ValueError, ica.plot_properties, epochs, plot_std=[])
assert_raises(ValueError, ica.plot_properties, ica)
assert_raises(ValueError, ica.plot_properties, [0.2])
assert_raises(ValueError, plot_ica_properties, epochs, epochs)
assert_raises(ValueError, ica.plot_properties, epochs,
psd_args='not dict')
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()[:-1]
ica.plot_properties(epochs, picks=1, axes=ax, **topoargs)
fig = ica.plot_properties(raw, picks=[0, 1], **topoargs)
assert_equal(len(fig), 2)
assert_raises(ValueError, plot_ica_properties, epochs, ica, picks=[0, 1],
axes=ax)
assert_raises(ValueError, ica.plot_properties, epochs, axes='not axes')
plt.close('all')
# Test merging grads.
raw = _get_raw(preload=True)
picks = pick_types(raw.info, meg='grad')[:10]
ica = ICA(n_components=2)
ica.fit(raw, picks=picks)
ica.plot_properties(raw)
plt.close('all')
def test_plot_ica_properties():
"""Test plotting of ICA properties."""
import matplotlib.pyplot as plt
raw = _get_raw(preload=True)
raw.add_proj([], remove_existing=True)
events = _get_events()
picks = _get_picks(raw)[:6]
pick_names = [raw.ch_names[k] for k in picks]
raw.pick_channels(pick_names)
with warnings.catch_warnings(record=True): # bad proj
epochs = Epochs(raw, events[:10], event_id, tmin, tmax,
baseline=(None, 0), preload=True)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=2, n_pca_components=2)
with warnings.catch_warnings(record=True): # bad proj
ica.fit(raw)
# test _create_properties_layout
fig, ax = _create_properties_layout()
assert_equal(len(ax), 5)
topoargs = dict(topomap_args={'res': 10})
ica.plot_properties(raw, picks=0, **topoargs)
ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
ica.plot_properties(epochs, picks=1, image_args={'sigma': 1.5},
topomap_args={'res': 10, 'colorbar': True},
psd_args={'fmax': 65.}, plot_std=False,
figsize=[4.5, 4.5])
plt.close('all')
assert_raises(ValueError, ica.plot_properties, epochs, dB=list('abc'))
assert_raises(ValueError, ica.plot_properties, epochs, plot_std=[])
assert_raises(ValueError, ica.plot_properties, ica)
assert_raises(ValueError, ica.plot_properties, [0.2])
assert_raises(ValueError, plot_ica_properties, epochs, epochs)
assert_raises(ValueError, ica.plot_properties, epochs,
psd_args='not dict')
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()[:-1]
ica.plot_properties(epochs, picks=1, axes=ax)
fig = ica.plot_properties(raw, picks=[0, 1], **topoargs)
assert_equal(len(fig), 2)
assert_raises(ValueError, plot_ica_properties, epochs, ica, picks=[0, 1],
axes=ax)
assert_raises(ValueError, ica.plot_properties, epochs, axes='not axes')
plt.close('all')
# Test merging grads.
raw = _get_raw(preload=True)
picks = pick_types(raw.info, meg='grad')[:10]
ica = ICA(n_components=2)
ica.fit(raw, picks=picks)
ica.plot_properties(raw)
plt.close('all')
def test_plot_ica_properties():
"""Test plotting of ICA properties."""
res = 8
raw = _get_raw(preload=True)
raw.add_proj([], remove_existing=True)
events = _get_events()
picks = _get_picks(raw)[:6]
pick_names = [raw.ch_names[k] for k in picks]
raw.pick_channels(pick_names)
reject = dict(grad=4000e-13, mag=4e-12)
epochs = Epochs(raw, events[:10], event_id, tmin, tmax,
baseline=(None, 0), preload=True)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=2, n_pca_components=2)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw)
# test _create_properties_layout
fig, ax = _create_properties_layout()
assert_equal(len(ax), 5)
topoargs = dict(topomap_args={'res': res, 'contours': 0, "sensors": False})
ica.plot_properties(raw, picks=0, **topoargs)
ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
ica.plot_properties(epochs, picks=1, image_args={'sigma': 1.5},
topomap_args={'res': 10, 'colorbar': True},
psd_args={'fmax': 65.}, plot_std=False,
figsize=[4.5, 4.5], reject=reject)
plt.close('all')
pytest.raises(TypeError, ica.plot_properties, epochs, dB=list('abc'))
pytest.raises(TypeError, ica.plot_properties, ica)
pytest.raises(TypeError, ica.plot_properties, [0.2])
pytest.raises(TypeError, plot_ica_properties, epochs, epochs)
pytest.raises(TypeError, ica.plot_properties, epochs,
psd_args='not dict')
pytest.raises(ValueError, ica.plot_properties, epochs, plot_std=[])
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()[:-1]
ica.plot_properties(epochs, picks=1, axes=ax, **topoargs)
fig = ica.plot_properties(raw, picks=[0, 1], **topoargs)
assert_equal(len(fig), 2)
pytest.raises(TypeError, plot_ica_properties, epochs, ica, picks=[0, 1],
axes=ax)
pytest.raises(ValueError, ica.plot_properties, epochs, axes='not axes')
plt.close('all')
# Test merging grads.
raw = _get_raw(preload=True)
picks = pick_types(raw.info, meg='grad')[:10]
ica = ICA(n_components=2)
ica.fit(raw, picks=picks)
ica.plot_properties(raw)
plt.close('all')
def test_plot_ica_properties():
"""Test plotting of ICA properties."""
import matplotlib.pyplot as plt
raw = _get_raw(preload=True)
raw.add_proj([], remove_existing=True)
events = _get_events()
picks = _get_picks(raw)[:6]
pick_names = [raw.ch_names[k] for k in picks]
raw.pick_channels(pick_names)
with warnings.catch_warnings(record=True): # bad proj
epochs = Epochs(raw, events[:10], event_id, tmin, tmax, baseline=(None, 0), preload=True)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2, max_pca_components=2, n_pca_components=2)
with warnings.catch_warnings(record=True): # bad proj
ica.fit(raw)
# test _create_properties_layout
fig, ax = _create_properties_layout()
assert_equal(len(ax), 5)
topoargs = dict(topomap_args={"res": 10})
ica.plot_properties(raw, picks=0, **topoargs)
ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
ica.plot_properties(
epochs,
picks=1,
image_args={"sigma": 1.5},
topomap_args={"res": 10, "colorbar": True},
psd_args={"fmax": 65.0},
plot_std=False,
figsize=[4.5, 4.5],
)
plt.close("all")
assert_raises(ValueError, ica.plot_properties, epochs, dB=list("abc"))
assert_raises(ValueError, ica.plot_properties, epochs, plot_std=[])
assert_raises(ValueError, ica.plot_properties, ica)
assert_raises(ValueError, ica.plot_properties, [0.2])
assert_raises(ValueError, plot_ica_properties, epochs, epochs)
assert_raises(ValueError, ica.plot_properties, epochs, psd_args="not dict")
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()[:-1]
ica.plot_properties(epochs, picks=1, axes=ax)
fig = ica.plot_properties(raw, picks=[0, 1], **topoargs)
assert_equal(len(fig), 2)
assert_raises(ValueError, plot_ica_properties, epochs, ica, picks=[0, 1], axes=ax)
assert_raises(ValueError, ica.plot_properties, epochs, axes="not axes")
plt.close("all")
def test_plot_ica_properties():
"""Test plotting of ICA properties."""
raw = _get_raw(preload=True).crop(0, 5)
raw.add_proj([], remove_existing=True)
with raw.info._unlock():
raw.info['highpass'] = 1.0 # fake high-pass filtering
events = make_fixed_length_events(raw)
picks = _get_picks(raw)[:6]
pick_names = [raw.ch_names[k] for k in picks]
raw.pick_channels(pick_names)
reject = dict(grad=4000e-13, mag=4e-12)
epochs = Epochs(raw,
events[:3],
event_id,
tmin,
tmax,
baseline=(None, 0),
preload=True)
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_iter=1,
random_state=0)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw)
# test _create_properties_layout
fig, ax = _create_properties_layout()
assert_equal(len(ax), 5)
with pytest.raises(ValueError, match='specify both fig and figsize'):
_create_properties_layout(figsize=(2, 2), fig=fig)
topoargs = dict(topomap_args={'res': 4, 'contours': 0, "sensors": False})
with catch_logging() as log:
ica.plot_properties(raw, picks=0, verbose='debug', **topoargs)
log = log.getvalue()
assert raw.ch_names[0] == 'MEG 0113'
assert 'Interpolation mode local to mean' in log, log
ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
ica.plot_properties(epochs,
picks=1,
image_args={'sigma': 1.5},
topomap_args={
'res': 4,
'colorbar': True
},
psd_args={'fmax': 65.},
plot_std=False,
figsize=[4.5, 4.5],
reject=reject)
plt.close('all')
with pytest.raises(TypeError, match='must be an instance'):
ica.plot_properties(epochs, dB=list('abc'))
with pytest.raises(TypeError, match='must be an instance'):
ica.plot_properties(ica)
with pytest.raises(TypeError, match='must be an instance'):
ica.plot_properties([0.2])
with pytest.raises(TypeError, match='must be an instance'):
plot_ica_properties(epochs, epochs)
with pytest.raises(TypeError, match='must be an instance'):
ica.plot_properties(epochs, psd_args='not dict')
with pytest.raises(TypeError, match='must be an instance'):
ica.plot_properties(epochs, plot_std=[])
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()[:-1]
ica.plot_properties(epochs, picks=1, axes=ax, **topoargs)
pytest.raises(TypeError,
plot_ica_properties,
epochs,
ica,
picks=[0, 1],
axes=ax)
pytest.raises(ValueError, ica.plot_properties, epochs, axes='not axes')
plt.close('all')
# Test merging grads.
pick_names = raw.ch_names[:15:2] + raw.ch_names[1:15:2]
raw = _get_raw(preload=True).pick_channels(pick_names).crop(0, 5)
raw.info.normalize_proj()
ica = ICA(random_state=0, max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw)
ica.plot_properties(raw)
plt.close('all')
# Test handling of zeros
ica = ICA(random_state=0, max_iter=1)
epochs.pick_channels(pick_names)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(epochs)
epochs._data[0] = 0
# Usually UserWarning: Infinite value .* for epo
with _record_warnings():
ica.plot_properties(epochs, **topoargs)
plt.close('all')
# Test Raw with annotations
annot = Annotations(onset=[1], duration=[1], description=['BAD'])
raw_annot = _get_raw(preload=True).set_annotations(annot).crop(0, 8)
raw_annot.pick(np.arange(10))
raw_annot.del_proj()
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw_annot)
# drop bad data segments
fig = ica.plot_properties(raw_annot, picks=[0, 1], **topoargs)
assert_equal(len(fig), 2)
# don't drop
ica.plot_properties(raw_annot, reject_by_annotation=False, **topoargs)
def test_plot_ica_properties():
"""Test plotting of ICA properties."""
res = 8
raw = _get_raw(preload=True)
raw.add_proj([], remove_existing=True)
events = _get_events()
picks = _get_picks(raw)[:6]
pick_names = [raw.ch_names[k] for k in picks]
raw.pick_channels(pick_names)
reject = dict(grad=4000e-13, mag=4e-12)
epochs = Epochs(raw,
events[:10],
event_id,
tmin,
tmax,
baseline=(None, 0),
preload=True)
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_iter=1,
max_pca_components=2,
n_pca_components=2,
random_state=0)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw)
# test _create_properties_layout
fig, ax = _create_properties_layout()
assert_equal(len(ax), 5)
topoargs = dict(topomap_args={'res': res, 'contours': 0, "sensors": False})
ica.plot_properties(raw, picks=0, **topoargs)
ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
ica.plot_properties(epochs,
picks=1,
image_args={'sigma': 1.5},
topomap_args={
'res': 10,
'colorbar': True
},
psd_args={'fmax': 65.},
plot_std=False,
figsize=[4.5, 4.5],
reject=reject)
plt.close('all')
pytest.raises(TypeError, ica.plot_properties, epochs, dB=list('abc'))
pytest.raises(TypeError, ica.plot_properties, ica)
pytest.raises(TypeError, ica.plot_properties, [0.2])
pytest.raises(TypeError, plot_ica_properties, epochs, epochs)
pytest.raises(TypeError, ica.plot_properties, epochs, psd_args='not dict')
pytest.raises(ValueError, ica.plot_properties, epochs, plot_std=[])
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()[:-1]
ica.plot_properties(epochs, picks=1, axes=ax, **topoargs)
fig = ica.plot_properties(raw, picks=[0, 1], **topoargs)
assert_equal(len(fig), 2)
pytest.raises(TypeError,
plot_ica_properties,
epochs,
ica,
picks=[0, 1],
axes=ax)
pytest.raises(ValueError, ica.plot_properties, epochs, axes='not axes')
plt.close('all')
# Test merging grads.
pick_names = raw.ch_names[:15:2] + raw.ch_names[1:15:2]
raw = _get_raw(preload=True).pick_channels(pick_names)
raw.info.normalize_proj()
ica = ICA(random_state=0, max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw)
ica.plot_properties(raw)
plt.close('all')
# Test handling of zeros
raw._data[:] = 0
with pytest.warns(None): # Usually UserWarning: Infinite value .* for epo
ica.plot_properties(raw)
ica = ICA(random_state=0, max_iter=1)
epochs.pick_channels(pick_names)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(epochs)
epochs._data[0] = 0
with pytest.warns(None): # Usually UserWarning: Infinite value .* for epo
ica.plot_properties(epochs)
plt.close('all')
# Test Raw with annotations
annot = Annotations(onset=[1], duration=[1], description=['BAD'])
raw_annot = _get_raw(preload=True).set_annotations(annot)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw_annot)
# drop bad data segments
ica.plot_properties(raw_annot)
# don't drop
ica.plot_properties(raw_annot, reject_by_annotation=False)
# fitting with bad data
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw_annot, reject_by_annotation=False)
# drop bad data when plotting
ica.plot_properties(raw_annot)
# don't drop bad data when plotting
ica.plot_properties(raw_annot, reject_by_annotation=False)
plt.close('all')
