def plot_multiple_ERP(epochs,
n_row=3,
n_col=2,
outfile=None,
title=None,
plot_topo=True):
"""
Plot ERP for the different conditions
"""
fig, ax = plt.subplots(n_row, n_col, sharex=True, figsize=(16, 9))
if title:
fig.suptitle(title)
conditions = list(epochs.event_id.keys())
if n_row * n_col != len(conditions):
raise ValueError("Size of the plot and number of experimental"
"conditions dont match!")
for col in range(n_col):
for row, condition in zip(range(n_row),
conditions[col * n_row:col * n_row + n_row]):
epochs[condition].average().plot(axes=ax[row, col])
ax[row, col].set_title("evoked response " + condition)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
if outfile:
fig.savefig(outfile)
plt.close()
if plot_topo:
evokeds = [epochs[condition].average() for condition in conditions]
plot_evoked_topo(evokeds)
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 plot_topo_compare():
"""This function will compare evoked potentials across conditions
given one channel to look at"""
import matplotlib.pyplot as plt
from mne.viz import plot_evoked_topo
include = [raw_input('Please select the channel you wish to look at: ')]
reject = dict(eeg=200e-6)
picks = mne.pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=True,
include=include,
exclude='bads')
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in event_id]
colors = 'yellow', 'green'
key, value = event_id
title = (
"Topographical map of evoked response comparing \n%s to %s as conditions"
% key, value)
plot_evoked_topo(evokeds, color=colors, title=title)
plt.show()
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_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]))
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_single_ch():
"""Test single channel topoplot with time cursor"""
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
fig = plot_evoked_topo(evoked)
num_figures_before = len(plt.get_fignums())
_fake_click(fig, fig.axes[0], (0.08, 0.65))
assert_equal(num_figures_before + 1, len(plt.get_fignums()))
fig = plt.gcf()
ax = plt.gca()
_fake_click(fig, ax, (.5, .5), kind='motion') # cursor should appear
assert_true(isinstance(ax._cursorline, matplotlib.lines.Line2D))
_fake_click(fig, ax, (1.5, 1.5), kind='motion') # cursor should disappear
assert_equal(ax._cursorline, None)
plt.close('all')
def test_plot_topo_single_ch():
"""Test single channel topoplot with time cursor."""
evoked = _get_epochs().average()
fig = plot_evoked_topo(evoked, background_color='w')
# test status bar message
ax = plt.gca()
assert ('MEG 0113' in ax.format_coord(.065, .63))
num_figures_before = len(plt.get_fignums())
_fake_click(fig, fig.axes[0], (0.08, 0.65))
assert num_figures_before + 1 == len(plt.get_fignums())
fig = plt.gcf()
ax = plt.gca()
_fake_click(fig, ax, (.5, .5), kind='motion') # cursor should appear
assert (isinstance(ax._cursorline, matplotlib.lines.Line2D))
_fake_click(fig, ax, (1.5, 1.5), kind='motion') # cursor should disappear
assert ax._cursorline is None
plt.close('all')
def plot_topo(epochs, conditions=OrderedDict()):
palette = sns.color_palette("hls", len(conditions) + 1)
evokeds = [epochs[name].average() for name in (conditions)]
evoked_topo = viz.plot_evoked_topo(evokeds,
vline=None,
color=palette[0:len(conditions)],
show=False)
evoked_topo.patch.set_alpha(0)
evoked_topo.set_size_inches(10, 8)
for axis in evoked_topo.axes:
for line in axis.lines:
line.set_linewidth(2)
legend_loc = 0
labels = [e.comment if e.comment else 'Unknown' for e in evokeds]
legend = plt.legend(labels, loc=legend_loc, prop={'size': 20})
txts = legend.get_texts()
for txt, col in zip(txts, palette):
txt.set_color(col)
return evoked_topo
reject = dict(grad=4000e-13, mag=4e-12)
# Create epochs including different events
event_id = {
'audio/left': 1,
'audio/right': 2,
'visual/left': 3,
'visual/right': 4
}
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
picks='meg',
baseline=(None, 0),
reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in ('left', 'right')]
# %%
# Show topography for two different conditions
colors = 'blue', 'red'
title = 'MNE sample data\nleft vs right (A/V combined)'
plot_evoked_topo(evokeds, color=colors, title=title, background_color='w')
plt.show()
# Set up amplitude-peak rejection values for MEG channels
reject = dict(grad=4000e-13, mag=4e-12)
# pick MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
include=include, exclude='bads')
# Create epochs including different events
event_id = {'audio/left': 1, 'audio/right': 2,
'visual/left': 3, 'visual/right': 4}
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
picks=picks, baseline=(None, 0), reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in ('left', 'right')]
###############################################################################
# Show topography for two different conditions
colors = 'yellow', 'green'
title = 'MNE sample data - left vs right (A/V combined)'
plot_evoked_topo(evokeds, color=colors, title=title)
conditions = [e.comment for e in evokeds]
for cond, col, pos in zip(conditions, colors, (0.025, 0.07)):
plt.figtext(0.99, pos, cond, color=col, fontsize=12,
horizontalalignment='right')
plt.show()
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')
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')
# Set up pick list: MEG + STI 014 - bad channels (modify to your needs)
include = [] # or stim channels ['STI 014']
# bad channels in raw.info['bads'] will be automatically excluded
# Set up amplitude-peak rejection values for MEG channels
reject = dict(grad=4000e-13, mag=4e-12)
# pick MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
include=include, exclude='bads')
# Create epochs including different events
event_id = {'audio/left': 1, 'audio/right': 2,
'visual/left': 3, 'visual/right': 4}
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
picks=picks, baseline=(None, 0), reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in ('left', 'right')]
###############################################################################
# Show topography for two different conditions
colors = 'blue', 'red'
title = 'MNE sample data\nleft vs right (A/V combined)'
plot_evoked_topo(evokeds, color=colors, title=title, background_color='w')
plt.show()
================================= Plot topographies for MEG sensors ================================= """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) import matplotlib.pyplot as plt from mne import read_evokeds from mne.viz import plot_evoked_topo from mne.datasets import sample print(__doc__) data_path = sample.data_path() fname = data_path + "/MEG/sample/sample_audvis-ave.fif" # Reading condition = "Left Auditory" evoked = read_evokeds(fname, condition=condition, baseline=(None, 0)) ############################################################################### # Show topography title = "MNE sample data (condition : %s)" % evoked.comment plot_evoked_topo(evoked, title=title) plt.show()
evoked_avg = [] #creates a LIST of evoked objects (dict)
for key in codes:
# create a copy of the first evoked object in ssavg[key]:
evoked_avg.append(ssavg[key][0].copy())
evoked_avg[-1].data = evoked_avg[-1].data * 0
n_ssavg = len(ssavg[key])
for i in range(n_ssavg):
if len(ssavg[key][i].data) == len(evoked_avg[-1].data):
# average the data accross participants:
evoked_avg[-1].data = evoked_avg[-1].data + ssavg[key][i].data / n_ssavg
print("Click on the thing to show zoomed-in electrode")
plot_evoked_topo(evoked_avg) #first argument is a list of evoked instances
#(2) manually remove bad epochs:
#epochs.drop_bad()
#epochs.plot_drop_log()
epochs.plot()
#(3) remember to save
#out_file = args.epochs_file.split('.')[0]+'-epo.fif'
print("If you want to save the file, do this in python shell:")
print("epochs.save(args.epochs_file)")
#epochs.save('Obja0004-epo.fif')
#epochs.save(args.epochs_file)
================================= """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) import matplotlib.pyplot as plt from mne import read_evokeds from mne.viz import plot_evoked_topo from mne.datasets import sample print(__doc__) data_path = sample.data_path() fname = data_path + '/MEG/sample/sample_audvis-ave.fif' # Reading condition = 'Left Auditory' evoked = read_evokeds(fname, condition=condition, baseline=(None, 0)) ############################################################################### # Show topography. # For Neuromag data, you can use merge_grads=True to plot # RMS values of gradiometer pairs. title = 'MNE sample data (condition : %s)' % evoked.comment plot_evoked_topo(evoked, title=title, merge_grads=False) plt.show()
psd_evo1 = psd_to_evo(psd1, freqs1, epo1, keep_ch_names=False) psd_evo2 = psd_to_evo(psd2, freqs2, epo2, keep_ch_names=False) # Not necessary, but you can name them. Useful for plotting psd_evo1.comment = "First" psd_evo2.comment = "Second" # Any Evoked can be plotted e.g. as a topo. # as far as MNE knows, this is an ERP, so the X and Y axis # will not be correctly titled, i.e. the X axis will say it's # time, when it's actually frequency. Just ignore for now. The # values themselves are correct. psd_evo1.plot_topo() # plot multiple plot_evoked_topo([psd_evo1, psd_evo2]) # subtract condition two from one # note this function is very general, and can perform any # linear operation on any set of Evokeds one_minus_two = combine_evoked([psd_evo1, psd_evo2], [1, -1]) one_minus_two.comment = "one - two" one_minus_two.plot_topo() # Note that the channels names have the generic, Neuromag names, i.e. # "MEGXXX." The automatic channel layout identifier gets # confused by the native "AXXX" Magnes3600 names, and so functions such as # plot_topo fail, because they cannot figure out the physical locations # of the channels. One way to solve this problem is to convert the channel # names to Neuromag names with psd_to_evo(keep_ch_names=False). This is # the approach used above. Alternatively, if you want to keep the Magnes
================================= Plot topographies for MEG sensors ================================= """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) import matplotlib.pyplot as plt from mne import read_evokeds from mne.viz import plot_evoked_topo from mne.datasets import sample print(__doc__) data_path = sample.data_path() fname = data_path + '/MEG/sample/sample_audvis-ave.fif' # Reading condition = 'Left Auditory' evoked = read_evokeds(fname, condition=condition, baseline=(None, 0)) ############################################################################### # Show topography title = 'MNE sample data (condition : %s)' % evoked.comment plot_evoked_topo(evoked, title=title) plt.show()
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')
def test_plot_topo_nirs(fnirs_evoked):
"""Test plotting of ERP topography for nirs data."""
fnirs_evoked.pick(picks='hbo')
fig = plot_evoked_topo(fnirs_evoked)
assert len(fig.axes) == 1
plt.close('all')
lh_aud = mne.Epochs(raw,
events,
event_id=1,
tmin=-0.75,
tmax=0.75,
baseline=(-0.75, -0.5))
rh_aud = mne.Epochs(raw,
events,
event_id=3,
tmin=-0.75,
tmax=0.75,
baseline=(-0.75, -0.5))
"""Compared evoked responses"""
if (plot_topography):
lh_aud_evoked, rh_aud_evoked = lh_aud.average(), rh_aud.average()
plot_evoked_topo([lh_aud_evoked, rh_aud_evoked], color=['blue', 'red'])
# Select all epochs and time points from one channel.
ch_ind = [
i for i, ch_name in enumerate(raw.info['ch_names'])
if 'MEG 2343' in ch_name
][0]
lh_data, rh_data = (lh_aud.get_data()[:,
ch_ind, :], rh_aud.get_data()[:,
ch_ind, :])
significant, pvals, cstats, ref_cstat, ref_clusters = permutation_test(
lh_data, rh_data)
plot_permutation_distribution(cstats, ref_cstat, show_plot=True)
plot_permutation_result_1d(lh_data, rh_data, significant, lh_aud.times,
ref_clusters)