def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
evoked = _get_epochs().average()
plot_topo(evoked, layout)
warnings.simplefilter('always', UserWarning)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_topo(picked_evoked_delayed_ssp, layout, proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', 'inaxes')
func(event(inaxes=fig.axes[0]))
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
evoked = _get_epochs().average()
plot_topo(evoked, layout)
warnings.simplefilter('always', UserWarning)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(
evoked_delayed_ssp, ch_names)
fig = plot_topo(picked_evoked_delayed_ssp, layout, proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', 'inaxes')
func(event(inaxes=fig.axes[0]))
def test_plot_topo():
"""Test plotting of ERP topography
"""
import matplotlib.pyplot as plt
# Show topography
evoked = _get_epochs().average()
# should auto-find layout
plot_evoked_topo([evoked, evoked], merge_grads=True)
# Test jointplot
evoked.plot_joint()
evoked.plot_joint(title='test',
ts_args=dict(spatial_colors=True),
topomap_args=dict(colorbar=True, times=[0.]))
warnings.simplefilter('always', UserWarning)
picked_evoked = evoked.copy().pick_channels(evoked.ch_names[:3])
picked_evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError,
plot_evoked_topo,
evo,
layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(
evoked_delayed_ssp, ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp,
layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', ['inaxes', 'xdata', 'ydata'])
func(
event(inaxes=fig.axes[0],
xdata=fig.axes[0]._mne_axs[0].pos[0],
ydata=fig.axes[0]._mne_axs[0].pos[1]))
func(event(inaxes=fig.axes[0], xdata=0, ydata=0))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig,
projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
_plot_update_evoked_topo_proj(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)),
proj=True)
picked_evoked.plot_topo(merge_grads=True) # Test RMS plot of grad pairs
plt.close('all')
for ax, idx in iter_topography(evoked.info):
ax.plot(evoked.data[idx], color='red')
plt.close('all')
def test_plot_topo():
"""Test plotting of ERP topography."""
# Show topography
evoked = _get_epochs().average()
# should auto-find layout
plot_evoked_topo([evoked, evoked], merge_grads=True, background_color='w')
picked_evoked = evoked.copy().pick_channels(evoked.ch_names[:3])
picked_evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
pytest.raises(ValueError, plot_evoked_topo, evo, layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp, layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', ['inaxes', 'xdata', 'ydata'])
func(event(inaxes=fig.axes[0], xdata=fig.axes[0]._mne_axs[0].pos[0],
ydata=fig.axes[0]._mne_axs[0].pos[1]))
func(event(inaxes=fig.axes[0], xdata=0, ydata=0))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig, projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
with pytest.warns(RuntimeWarning, match='projection'):
_plot_update_evoked_topo_proj(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)), proj=True,
background_color='k')
# Test RMS plot of grad pairs
picked_evoked.plot_topo(merge_grads=True, background_color='w')
plt.close('all')
for ax, idx in iter_topography(evoked.info):
ax.plot(evoked.data[idx], color='red')
# test status bar message
assert (evoked.ch_names[idx] in ax.format_coord(.5, .5))
plt.close('all')
cov = read_cov(cov_fname)
cov['projs'] = []
evoked.pick_types(meg=True).plot_topo(noise_cov=cov)
plt.close('all')
# test plot_topo
evoked.plot_topo() # should auto-find layout
_line_plot_onselect(0, 200, ['mag', 'grad'], evoked.info, evoked.data,
evoked.times)
plt.close('all')
def test_plot_topo():
"""Test plotting of ERP topography."""
import matplotlib.pyplot as plt
# Show topography
evoked = _get_epochs().average()
# should auto-find layout
plot_evoked_topo([evoked, evoked], merge_grads=True, background_color='w')
# Test jointplot
evoked.plot_joint()
def return_inds(d): # to test function kwarg to zorder arg of evoked.plot
return list(range(d.shape[0]))
evoked.plot_joint(title='test', topomap_args=dict(contours=0, res=8),
ts_args=dict(spatial_colors=True, zorder=return_inds))
assert_raises(ValueError, evoked.plot_joint, ts_args=dict(axes=True))
warnings.simplefilter('always', UserWarning)
picked_evoked = evoked.copy().pick_channels(evoked.ch_names[:3])
picked_evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_evoked_topo, evo, layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp, layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', ['inaxes', 'xdata', 'ydata'])
func(event(inaxes=fig.axes[0], xdata=fig.axes[0]._mne_axs[0].pos[0],
ydata=fig.axes[0]._mne_axs[0].pos[1]))
func(event(inaxes=fig.axes[0], xdata=0, ydata=0))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig, projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
_plot_update_evoked_topo_proj(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)), proj=True,
background_color='k')
# Test RMS plot of grad pairs
picked_evoked.plot_topo(merge_grads=True, background_color='w')
plt.close('all')
for ax, idx in iter_topography(evoked.info):
ax.plot(evoked.data[idx], color='red')
# test status bar message
assert_true(evoked.ch_names[idx] in ax.format_coord(.5, .5))
plt.close('all')
def test_plot_evoked_field():
"""Test plotting evoked field."""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
with warnings.catch_warnings(record=True): # bad proj
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1,
ch_type=t)
evoked.plot_field(maps, time=0.1)
def test_plot_evoked_field(renderer):
"""Test plotting evoked field."""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
with pytest.warns(RuntimeWarning, match='projection'):
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1,
ch_type=t)
evoked.plot_field(maps, time=0.1)
def test_plot_evoked_field():
"""Test plotting evoked field."""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
with pytest.warns(RuntimeWarning, match='projection'):
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1,
ch_type=t)
evoked.plot_field(maps, time=0.1)
def test_plot_evoked_field():
"""Test plotting evoked field
"""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
with warnings.catch_warnings(record=True): # bad proj
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1,
ch_type=t)
evoked.plot_field(maps, time=0.1)
def test_plot_evoked_field():
"""Test plotting evoked field
"""
trans_fname = op.join(data_dir, "MEG", "sample", "sample_audvis_raw-trans.fif")
evoked = read_evokeds(evoked_fname, condition="Left Auditory", baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ["meg", None]:
maps = make_field_map(
evoked, trans_fname=trans_fname, subject="sample", subjects_dir=subjects_dir, n_jobs=1, ch_type=t
)
evoked.plot_field(maps, time=0.1)
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
evoked = _get_epochs().average()
plot_topo(evoked, layout)
warnings.simplefilter("always", UserWarning)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=["y", "b"])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp, ch_names)
plot_topo(picked_evoked_delayed_ssp, layout, proj="interactive")
def test_plot_evoked_field():
"""Test plotting evoked field
"""
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
maps = make_field_map(evoked, trans_fname=trans_fname,
subject='sample', subjects_dir=subjects_dir,
n_jobs=1, ch_type=t)
evoked.plot_field(maps, time=0.1)
def test_plot_evoked_field(renderer):
"""Test plotting evoked field."""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
with pytest.warns(RuntimeWarning, match='projection'):
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1,
ch_type=t)
fig = evoked.plot_field(maps, time=0.1)
if renderer.get_3d_backend() == 'mayavi':
import mayavi # noqa: F401 analysis:ignore
assert isinstance(fig, mayavi.core.scene.Scene)
def test_plot_evoked_field(backends_3d):
"""Test plotting evoked field."""
backend_name = get_3d_backend()
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
with pytest.warns(RuntimeWarning, match='projection'):
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1,
ch_type=t)
fig = evoked.plot_field(maps, time=0.1)
if backend_name == 'mayavi':
import mayavi # noqa: F401 analysis:ignore
assert isinstance(fig, mayavi.core.scene.Scene)
def test_plot_topo():
"""Test plotting of ERP topography
"""
import matplotlib.pyplot as plt
# Show topography
evoked = _get_epochs().average()
plot_evoked_topo(evoked) # should auto-find layout
# Test jointplot
evoked.plot_joint()
evoked.plot_joint(title='test', ts_args=dict(spatial_colors=True),
topomap_args=dict(colorbar=True, times=[0.]))
warnings.simplefilter('always', UserWarning)
picked_evoked = evoked.pick_channels(evoked.ch_names[:3], copy=True)
picked_evoked_eeg = evoked.pick_types(meg=False, eeg=True, copy=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_evoked_topo, evo, layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp, layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', ['inaxes', 'xdata', 'ydata'])
func(event(inaxes=fig.axes[0], xdata=fig.axes[0]._mne_axs[0].pos[0],
ydata=fig.axes[0]._mne_axs[0].pos[1]))
func(event(inaxes=fig.axes[0], xdata=0, ydata=0))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig, projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
_plot_update_evoked_topo_proj(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)), proj=True)
picked_evoked.plot_topo(merge_grads=True) # Test RMS plot of grad pairs
plt.close('all')
def test_plot_topo():
"""Test plotting of ERP topography
"""
import matplotlib.pyplot as plt
# Show topography
evoked = _get_epochs().average()
plot_evoked_topo(evoked) # should auto-find layout
warnings.simplefilter('always', UserWarning)
picked_evoked = evoked.pick_channels(evoked.ch_names[:3], copy=True)
picked_evoked_eeg = evoked.pick_types(meg=False, eeg=True, copy=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError,
plot_evoked_topo,
evo,
layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(
evoked_delayed_ssp, ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp,
layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', 'inaxes')
func(event(inaxes=fig.axes[0]))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig,
projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
_plot_update_evoked_topo(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)),
proj=True)
plt.close('all')
def test_plot_topo():
"""Test plotting of ERP topography."""
# Show topography
evoked = _get_epochs().average()
# should auto-find layout
plot_evoked_topo([evoked, evoked], merge_grads=True, background_color='w')
picked_evoked = evoked.copy().pick_channels(evoked.ch_names[:3])
picked_evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
pytest.raises(ValueError,
plot_evoked_topo,
evo,
layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp,
layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', ['inaxes', 'xdata', 'ydata'])
func(
event(inaxes=fig.axes[0],
xdata=fig.axes[0]._mne_axs[0].pos[0],
ydata=fig.axes[0]._mne_axs[0].pos[1]))
func(event(inaxes=fig.axes[0], xdata=0, ydata=0))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig,
projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
with pytest.warns(RuntimeWarning, match='projection'):
_plot_update_evoked_topo_proj(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)),
proj=True,
background_color='k')
# Test RMS plot of grad pairs
picked_evoked.plot_topo(merge_grads=True, background_color='w')
plt.close('all')
for ax, idx in iter_topography(evoked.info, legend=True):
ax.plot(evoked.data[idx], color='red')
# test status bar message
if idx != -1:
assert (evoked.ch_names[idx] in ax.format_coord(.5, .5))
assert idx == -1
plt.close('all')
cov = read_cov(cov_fname)
cov['projs'] = []
evoked.pick_types(meg=True).plot_topo(noise_cov=cov)
plt.close('all')
# test plot_topo
evoked.plot_topo() # should auto-find layout
_line_plot_onselect(0, 200, ['mag', 'grad'], evoked.info, evoked.data,
evoked.times)
plt.close('all')
for ax, idx in iter_topography(evoked.info): # brief test with false
ax.plot([0, 1, 2])
break
plt.close('all')
from mne.minimum_norm import make_inverse_operator, apply_inverse
from mne.inverse_sparse import tf_mixed_norm
from mne.viz import plot_sparse_source_estimates
data_path = sample.data_path()
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
# Read noise covariance matrix
cov = mne.read_cov(cov_fname)
# Handling average file
condition = 'Left visual'
evoked = io.read_evokeds(ave_fname, condition=condition, baseline=(None, 0))
evoked = mne.pick_channels_evoked(evoked)
# We make the window slightly larger than what you'll eventually be interested
# in ([-0.05, 0.3]) to avoid edge effects.
evoked.crop(tmin=-0.1, tmax=0.4)
# Handling forward solution
forward = mne.read_forward_solution(fwd_fname, force_fixed=False,
surf_ori=True)
cov = mne.cov.regularize(cov, evoked.info)
###############################################################################
# Run solver
# alpha_space regularization parameter is between 0 and 100 (100 is high)
alpha_space = 50. # spatial regularization parameter
def test_plot_topo():
"""Test plotting of ERP topography."""
import matplotlib.pyplot as plt
# Show topography
evoked = _get_epochs().average()
# should auto-find layout
plot_evoked_topo([evoked, evoked], merge_grads=True, background_color='w')
# Test jointplot
evoked.plot_joint(ts_args=dict(time_unit='s'),
topomap_args=dict(time_unit='s'))
def return_inds(d): # to test function kwarg to zorder arg of evoked.plot
return list(range(d.shape[0]))
evoked.plot_joint(title='test',
topomap_args=dict(contours=0, res=8, time_unit='ms'),
ts_args=dict(spatial_colors=True,
zorder=return_inds,
time_unit='s'))
pytest.raises(ValueError,
evoked.plot_joint,
ts_args=dict(axes=True, time_unit='s'))
axes = plt.subplots(nrows=3)[-1].flatten().tolist()
evoked.plot_joint(times=[0],
picks=[6, 7, 8],
ts_args=dict(axes=axes[0]),
topomap_args={
"axes": axes[1:],
"time_unit": "s"
})
plt.close()
pytest.raises(ValueError,
evoked.plot_joint,
picks=[6, 7, 8],
ts_args=dict(axes=axes[0]),
topomap_args=dict(axes=axes[2:]))
warnings.simplefilter('always', UserWarning)
picked_evoked = evoked.copy().pick_channels(evoked.ch_names[:3])
picked_evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
pytest.raises(ValueError,
plot_evoked_topo,
evo,
layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(
evoked_delayed_ssp, ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp,
layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', ['inaxes', 'xdata', 'ydata'])
func(
event(inaxes=fig.axes[0],
xdata=fig.axes[0]._mne_axs[0].pos[0],
ydata=fig.axes[0]._mne_axs[0].pos[1]))
func(event(inaxes=fig.axes[0], xdata=0, ydata=0))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig,
projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
_plot_update_evoked_topo_proj(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)),
proj=True,
background_color='k')
# Test RMS plot of grad pairs
picked_evoked.plot_topo(merge_grads=True, background_color='w')
plt.close('all')
for ax, idx in iter_topography(evoked.info):
ax.plot(evoked.data[idx], color='red')
# test status bar message
assert (evoked.ch_names[idx] in ax.format_coord(.5, .5))
plt.close('all')
cov = read_cov(cov_fname)
cov['projs'] = []
evoked.pick_types(meg=True).plot_topo(noise_cov=cov)
plt.close('all')
vertno = [ss['vertno'] for ss in forward['src']]
stc_ctf = SourceEstimate(np.squeeze(ctfs).T, vertno, tmin=0., tstep=0.001)
# save CTF as STC file for mne_analyze
print "STC filename for CTFs: %s " % stc_fname_out
stc_ctf.save(stc_fname_out)
#### APPLY TO EVOKED DATA AND COMPUTE SNRs
# (e.g. to check whether regularization is reasonable)
# (assumes as many conditions in evoked as spatial filters in spatial_filters)
# read and prepare evoked data
evoked = [mne.read_evokeds(fname_evoked, condition=cc, baseline=(None, 0),
proj=True) for cc in range(n_filter)]
evoked = [mne.pick_channels_evoked(ee, ch_names) for ee in evoked]
evoked_mat = [ee.data for ee in evoked]
# Apply estimator to data
evoked_tc = [spatial_filters[ff].dot(evoked_mat[ff]) for ff in range(n_filter)]
# Compute SNR as ration with standard deviation of baseline (to 0ms)
base_idx = [np.abs(ee.times).argmin() for ee in evoked]
evoked_tc_snr = [evoked_tc[ff] / np.std(evoked_tc[:base_idx[ff]]) for ff
in range(n_filter)]
print "\n Evoked data:"
for ff in np.arange(len(spatial_filters)):
print "Max SNR (std to baseline) %f: " % np.abs(evoked_tc_snr[ff]).max()
print "\n"
print(__doc__) data_path = sample.data_path() subjects_dir = data_path + '/subjects' fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif' cov_fname = data_path + '/MEG/sample/sample_audvis-shrunk-cov.fif' # Read noise covariance matrix cov = mne.read_cov(cov_fname) # Handling average file condition = 'Left visual' evoked = mne.read_evokeds(ave_fname, condition=condition, baseline=(None, 0)) evoked = mne.pick_channels_evoked(evoked) # We make the window slightly larger than what you'll eventually be interested # in ([-0.05, 0.3]) to avoid edge effects. evoked.crop(tmin=-0.1, tmax=0.4) # Handling forward solution forward = mne.read_forward_solution(fwd_fname) ############################################################################### # Run solver # alpha parameter is between 0 and 100 (100 gives 0 active source) alpha = 40. # general regularization parameter # l1_ratio parameter between 0 and 1 promotes temporal smoothness # (0 means no temporal regularization) l1_ratio = 0.03 # temporal regularization parameter
def test_plot_topo():
"""Test plotting of ERP topography."""
# Show topography
evoked = _get_epochs().average()
# should auto-find layout
plot_evoked_topo([evoked, evoked], merge_grads=True, background_color='w')
# Test jointplot
evoked.plot_joint(ts_args=dict(time_unit='s'),
topomap_args=dict(time_unit='s'))
def return_inds(d): # to test function kwarg to zorder arg of evoked.plot
return list(range(d.shape[0]))
evoked.plot_joint(title='test', topomap_args=dict(contours=0, res=8,
time_unit='ms'),
ts_args=dict(spatial_colors=True, zorder=return_inds,
time_unit='s'))
pytest.raises(ValueError, evoked.plot_joint, ts_args=dict(axes=True,
time_unit='s'))
axes = plt.subplots(nrows=3)[-1].flatten().tolist()
evoked.plot_joint(times=[0], picks=[6, 7, 8], ts_args=dict(axes=axes[0]),
topomap_args={"axes": axes[1:], "time_unit": "s"})
plt.close()
pytest.raises(ValueError, evoked.plot_joint, picks=[6, 7, 8],
ts_args=dict(axes=axes[0]), topomap_args=dict(axes=axes[2:]))
picked_evoked = evoked.copy().pick_channels(evoked.ch_names[:3])
picked_evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
for ylim in [dict(mag=[-600, 600]), None]:
plot_evoked_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
pytest.raises(ValueError, plot_evoked_topo, evo, layout,
color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_evoked_topo(picked_evoked_delayed_ssp, layout,
proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', ['inaxes', 'xdata', 'ydata'])
func(event(inaxes=fig.axes[0], xdata=fig.axes[0]._mne_axs[0].pos[0],
ydata=fig.axes[0]._mne_axs[0].pos[1]))
func(event(inaxes=fig.axes[0], xdata=0, ydata=0))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig, projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
with pytest.warns(RuntimeWarning, match='projection'):
_plot_update_evoked_topo_proj(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)), proj=True,
background_color='k')
# Test RMS plot of grad pairs
picked_evoked.plot_topo(merge_grads=True, background_color='w')
plt.close('all')
for ax, idx in iter_topography(evoked.info):
ax.plot(evoked.data[idx], color='red')
# test status bar message
assert (evoked.ch_names[idx] in ax.format_coord(.5, .5))
plt.close('all')
cov = read_cov(cov_fname)
cov['projs'] = []
evoked.pick_types(meg=True).plot_topo(noise_cov=cov)
plt.close('all')
# test plot_topo
evoked.plot_topo() # should auto-find layout
_line_plot_onselect(0, 200, ['mag', 'grad'], evoked.info, evoked.data,
evoked.times)
plt.close('all')