def test_plot_ica_components():
"""Test plotting of ICA solutions."""
import matplotlib.pyplot as plt
res = 8
fast_test = {"res": res, "contours": 0, "sensors": False}
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_pca_components=3,
n_pca_components=3)
ica_picks = _get_picks(raw)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=ica_picks)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components,
image_interp='bilinear',
colorbar=True,
**fast_test)
plt.close('all')
# test interactive mode (passing 'inst' arg)
ica.plot_components([0, 1], image_interp='bilinear', inst=raw, res=16)
fig = plt.gcf()
# test title click
# ----------------
lbl = fig.axes[1].get_label()
ica_idx = int(lbl[-3:])
titles = [ax.title for ax in fig.axes]
title_pos_midpoint = (titles[1].get_window_extent().extents.reshape(
(2, 2)).mean(axis=0))
# first click adds to exclude
_fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
assert ica_idx in ica.exclude
# clicking again removes from exclude
_fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
assert ica_idx not in ica.exclude
# test topo click
# ---------------
_fake_click(fig, fig.axes[1], (0., 0.), xform='data')
c_fig = plt.gcf()
labels = [ax.get_label() for ax in c_fig.axes]
for l in ['topomap', 'image', 'erp', 'spectrum', 'variance']:
assert_true(l in labels)
topomap_ax = c_fig.axes[labels.index('topomap')]
title = topomap_ax.get_title()
assert_true(lbl == title)
ica.info = None
assert_raises(ValueError, ica.plot_components, 1)
assert_raises(RuntimeError, ica.plot_components, 1, ch_type='mag')
plt.close('all')
def test_plot_ica_topomap():
"""Test plotting of ICA solutions
"""
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica.decompose_raw(raw, picks=ica_picks)
for components in [0, [0], [0, 1], [0, 1] * 7]:
ica.plot_topomap(components)
ica.info = None
assert_raises(RuntimeError, ica.plot_topomap, 1)
def test_plot_ica_components():
"""Test plotting of ICA solutions."""
res = 8
fast_test = {"res": res, "contours": 0, "sensors": False}
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2)
ica_picks = _get_picks(raw)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw, picks=ica_picks)
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components, image_interp='bilinear',
colorbar=True, **fast_test)
plt.close('all')
# test interactive mode (passing 'inst' arg)
with catch_logging() as log:
ica.plot_components([0, 1], image_interp='bilinear', inst=raw, res=16,
verbose='debug', ch_type='grad')
log = log.getvalue()
assert 'grad data' in log
assert 'Interpolation mode local to mean' in log
fig = plt.gcf()
# test title click
# ----------------
lbl = fig.axes[1].get_label()
ica_idx = int(lbl[-3:])
titles = [ax.title for ax in fig.axes]
title_pos_midpoint = (titles[1].get_window_extent().extents
.reshape((2, 2)).mean(axis=0))
# first click adds to exclude
_fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
assert ica_idx in ica.exclude
# clicking again removes from exclude
_fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
assert ica_idx not in ica.exclude
# test topo click
# ---------------
_fake_click(fig, fig.axes[1], (0., 0.), xform='data')
c_fig = plt.gcf()
labels = [ax.get_label() for ax in c_fig.axes]
for label in ['topomap', 'image', 'erp', 'spectrum', 'variance']:
assert label in labels
topomap_ax = c_fig.axes[labels.index('topomap')]
title = topomap_ax.get_title()
assert (lbl == title)
ica.info = None
with pytest.raises(RuntimeError, match='fit the ICA'):
ica.plot_components(1, ch_type='mag')
def test_plot_ica_components():
"""Test plotting of ICA solutions."""
import matplotlib.pyplot as plt
res = 8
fast_test = {"res": res, "contours": 0, "sensors": False}
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica_picks = _get_picks(raw)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=ica_picks)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components, image_interp='bilinear',
colorbar=True, **fast_test)
plt.close('all')
# test interactive mode (passing 'inst' arg)
ica.plot_components([0, 1], image_interp='bilinear', inst=raw, res=16)
fig = plt.gcf()
# test title click
# ----------------
lbl = fig.axes[1].get_label()
ica_idx = int(lbl[-3:])
titles = [ax.title for ax in fig.axes]
title_pos_midpoint = (titles[1].get_window_extent().extents
.reshape((2, 2)).mean(axis=0))
# first click adds to exclude
_fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
assert ica_idx in ica.exclude
# clicking again removes from exclude
_fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
assert ica_idx not in ica.exclude
# test topo click
# ---------------
_fake_click(fig, fig.axes[1], (0., 0.), xform='data')
c_fig = plt.gcf()
labels = [ax.get_label() for ax in c_fig.axes]
for l in ['topomap', 'image', 'erp', 'spectrum', 'variance']:
assert_true(l in labels)
topomap_ax = c_fig.axes[labels.index('topomap')]
title = topomap_ax.get_title()
assert_true(lbl == title)
ica.info = None
assert_raises(ValueError, ica.plot_components, 1)
assert_raises(RuntimeError, ica.plot_components, 1, ch_type='mag')
plt.close('all')
def test_plot_ica_topomap():
"""Test plotting of ICA solutions
"""
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica_picks = fiff.pick_types(raw.info, meg=True, eeg=False, stim=False,
ecg=False, eog=False, exclude='bads')
ica.decompose_raw(raw, picks=ica_picks)
for components in [0, [0], [0, 1], [0, 1] * 7]:
ica.plot_topomap(components)
ica.info = None
assert_raises(RuntimeError, ica.plot_topomap, 1)
plt.close('all')
def test_plot_ica_topomap():
"""Test plotting of ICA solutions
"""
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2, max_pca_components=3, n_pca_components=3)
ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False, ecg=False, eog=False, exclude="bads")
ica.decompose_raw(raw, picks=ica_picks)
warnings.simplefilter("always", UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 7]:
ica.plot_topomap(components)
ica.info = None
assert_raises(RuntimeError, ica.plot_topomap, 1)
plt.close("all")
def test_plot_ica_components():
"""Test plotting of ICA solutions
"""
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica_picks = _get_picks(raw)
ica.fit(raw, picks=ica_picks)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components, image_interp='bilinear', res=16)
ica.info = None
assert_raises(RuntimeError, ica.plot_components, 1)
plt.close('all')
def plot_components(A, raw, picks, **kwargs):
p, q = A.shape
ica = ICA_(n_components=q, method='fastica', random_state=0,
fit_params=dict(max_iter=1))
ica.info = pick_info(raw.info, picks)
if ica.info['comps']:
ica.info['comps'] = []
ica.ch_names = ica.info['ch_names']
ica.mixing_matrix_ = A
ica.pca_components_ = np.eye(p)
ica.pca_mean_ = None
ica.unmixing_matrix_ = np.linalg.pinv(A)
ica.n_components_ = p
ica._update_ica_names()
ica.plot_components(**kwargs)
def test_plot_ica_components():
"""Test plotting of ICA solutions."""
import matplotlib.pyplot as plt
res = 8
fast_test = {"res": res, "contours": 0, "sensors": False}
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_pca_components=3,
n_pca_components=3)
ica_picks = _get_picks(raw)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=ica_picks)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components,
image_interp='bilinear',
colorbar=True,
**fast_test)
# test interactive mode (passing 'inst' arg)
plt.close('all')
ica.plot_components([0, 1], image_interp='bilinear', inst=raw, res=16)
fig = plt.gcf()
ax = [a for a in fig.get_children() if isinstance(a, plt.Axes)]
lbl = ax[1].get_label()
_fake_click(fig, ax[1], (0., 0.), xform='data')
c_fig = plt.gcf()
ax = [a for a in c_fig.get_children() if isinstance(a, plt.Axes)]
labels = [a.get_label() for a in ax]
for l in ['topomap', 'image', 'erp', 'spectrum', 'variance']:
assert_true(l in labels)
topomap_ax = ax[labels.index('topomap')]
title = topomap_ax.get_title()
assert_true(lbl == title)
ica.info = None
assert_raises(ValueError, ica.plot_components, 1)
assert_raises(RuntimeError, ica.plot_components, 1, ch_type='mag')
plt.close('all')
def transfer_to_mne(A, raw, picks):
'''
Hack to use the MNE ICA class providing the estimated mixing matrix A.
'''
p, q = A.shape
ica = ICA_(n_components=q, method='fastica', random_state=0,
fit_params=dict(max_iter=1))
ica.info = pick_info(raw.info, picks)
if ica.info['comps']:
ica.info['comps'] = []
ica.ch_names = ica.info['ch_names']
ica.mixing_matrix_ = A
ica.pca_components_ = np.eye(p)
ica.pca_mean_ = None
ica.unmixing_matrix_ = np.linalg.pinv(A)
ica.n_components_ = p
ica._update_ica_names()
return ica
def test_plot_ica_components():
"""Test plotting of ICA solutions."""
import matplotlib.pyplot as plt
res = 8
fast_test = {"res": res, "contours": 0, "sensors": False}
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica_picks = _get_picks(raw)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=ica_picks)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components, image_interp='bilinear',
colorbar=True, **fast_test)
# test interactive mode (passing 'inst' arg)
plt.close('all')
ica.plot_components([0, 1], image_interp='bilinear', inst=raw, res=16)
fig = plt.gcf()
ax = [a for a in fig.get_children() if isinstance(a, plt.Axes)]
lbl = ax[1].get_label()
_fake_click(fig, ax[1], (0., 0.), xform='data')
c_fig = plt.gcf()
ax = [a for a in c_fig.get_children() if isinstance(a, plt.Axes)]
labels = [a.get_label() for a in ax]
for l in ['topomap', 'image', 'erp', 'spectrum', 'variance']:
assert_true(l in labels)
topomap_ax = ax[labels.index('topomap')]
title = topomap_ax.get_title()
assert_true(lbl == title)
ica.info = None
assert_raises(ValueError, ica.plot_components, 1)
assert_raises(RuntimeError, ica.plot_components, 1, ch_type='mag')
plt.close('all')
def test_plot_ica_topomap():
"""Test plotting of ICA solutions
"""
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_pca_components=3,
n_pca_components=3)
ica_picks = fiff.pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
ecg=False,
eog=False,
exclude='bads')
ica.decompose_raw(raw, picks=ica_picks)
for components in [0, [0], [0, 1], [0, 1] * 7]:
ica.plot_topomap(components)
ica.info = None
assert_raises(RuntimeError, ica.plot_topomap, 1)
plt.close('all')
def test_plot_ica_components():
"""Test plotting of ICA solutions."""
import matplotlib.pyplot as plt
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2, max_pca_components=3, n_pca_components=3)
ica_picks = _get_picks(raw)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=ica_picks)
warnings.simplefilter("always", UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components, image_interp="bilinear", res=16, colorbar=True)
# test interactive mode (passing 'inst' arg)
plt.close("all")
ica.plot_components([0, 1], image_interp="bilinear", res=16, inst=raw)
fig = plt.gcf()
ax = [a for a in fig.get_children() if isinstance(a, plt.Axes)]
lbl = ax[1].get_label()
_fake_click(fig, ax[1], (0.0, 0.0), xform="data")
c_fig = plt.gcf()
ax = [a for a in c_fig.get_children() if isinstance(a, plt.Axes)]
labels = [a.get_label() for a in ax]
for l in ["topomap", "image", "erp", "spectrum", "variance"]:
assert_true(l in labels)
topomap_ax = ax[labels.index("topomap")]
title = topomap_ax.get_title()
assert_true(lbl == title)
ica.info = None
assert_raises(ValueError, ica.plot_components, 1)
assert_raises(RuntimeError, ica.plot_components, 1, ch_type="mag")
plt.close("all")
def test_plot_ica_topomap():
"""Test plotting of ICA solutions
"""
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_pca_components=3,
n_pca_components=3)
ica_picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
ecg=False,
eog=False,
exclude='bads')
ica.decompose_raw(raw, picks=ica_picks)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 7]:
ica.plot_topomap(components)
ica.info = None
assert_raises(RuntimeError, ica.plot_topomap, 1)
plt.close('all')
