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_evoked():
"""Test plotting of evoked."""
import matplotlib.pyplot as plt
rng = np.random.RandomState(0)
evoked = _get_epochs().average()
fig = evoked.plot(proj=True, hline=[1], exclude=[], window_title='foo',
time_unit='s')
# 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 & spatial_colors & zorder
evoked.plot(exclude='bads', time_unit='s')
# test selective updating of dict keys is working.
evoked.plot(hline=[1], units=dict(mag='femto foo'), time_unit='s')
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
evoked_delayed_ssp.plot(proj='interactive', time_unit='s')
evoked_delayed_ssp.apply_proj()
assert_raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', time_unit='s')
evoked_delayed_ssp.info['projs'] = []
assert_raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', time_unit='s')
assert_raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', axes='foo', time_unit='s')
plt.close('all')
# test GFP only
evoked.plot(gfp='only', time_unit='s')
assert_raises(ValueError, evoked.plot, gfp='foo', time_unit='s')
evoked.plot_image(proj=True, time_unit='ms')
# test mask
evoked.plot_image(picks=[1, 2], mask=evoked.data > 0, time_unit='s')
evoked.plot_image(picks=[1, 2], mask_cmap=None, colorbar=False,
mask=np.ones(evoked.data.shape).astype(bool),
time_unit='s')
with warnings.catch_warnings(record=True) as w:
evoked.plot_image(picks=[1, 2], mask=None, mask_style="both",
time_unit='s')
assert len(w) == 2
assert_raises(ValueError, evoked.plot_image, mask=evoked.data[1:, 1:] > 0,
time_unit='s')
# plot with bad channels excluded
evoked.plot_image(exclude='bads', cmap='interactive', time_unit='s')
evoked.plot_image(exclude=evoked.info['bads'], time_unit='s') # same thing
plt.close('all')
assert_raises(ValueError, evoked.plot_image, picks=[0, 0],
time_unit='s') # duplicates
evoked.plot_topo() # should auto-find layout
_line_plot_onselect(0, 200, ['mag', 'grad'], evoked.info, evoked.data,
evoked.times)
plt.close('all')
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
cov['projs'] = [] # avoid warnings
# test rank param.
evoked.plot_white(cov, rank={'mag': 101, 'grad': 201}, time_unit='s')
evoked.plot_white(cov, rank={'mag': 101}, time_unit='s') # test rank param
evoked.plot_white(cov, rank={'grad': 201}, time_unit='s')
assert_raises(
ValueError, evoked.plot_white, cov,
rank={'mag': 101, 'grad': 201, 'meg': 306}, time_unit='s')
assert_raises(
ValueError, evoked.plot_white, cov, rank={'meg': 306}, time_unit='s')
evoked.plot_white([cov, cov], time_unit='s')
# plot_compare_evokeds: test condition contrast, CI, color assignment
plot_compare_evokeds(evoked.copy().pick_types(meg='mag'))
plot_compare_evokeds(
evoked.copy().pick_types(meg='grad'), picks=[1, 2],
show_sensors="upper right", show_legend="upper left")
evokeds = [evoked.copy() for _ in range(10)]
for evoked in evokeds:
evoked.data += (rng.randn(*evoked.data.shape) *
np.std(evoked.data, axis=-1, keepdims=True))
for picks in ([0], [1], [2], [0, 2], [1, 2], [0, 1, 2],):
figs = plot_compare_evokeds([evokeds], picks=picks, ci=0.95)
if not isinstance(figs, list):
figs = [figs]
for fig in figs:
ext = fig.axes[0].collections[0].get_paths()[0].get_extents()
xs, ylim = ext.get_points().T
assert_allclose(xs, evoked.times[[0, -1]])
line = fig.axes[0].lines[0]
xs = line.get_xdata()
assert_allclose(xs, evoked.times)
ys = line.get_ydata()
assert (ys < ylim[1]).all()
assert (ys > ylim[0]).all()
plt.close('all')
evoked.rename_channels({'MEG 2142': "MEG 1642"})
assert len(plot_compare_evokeds(evoked)) == 2
colors = dict(red='r', blue='b')
linestyles = dict(red='--', blue='-')
red, blue = evoked.copy(), evoked.copy()
red.data *= 1.1
blue.data *= 0.9
plot_compare_evokeds([red, blue], picks=3) # list of evokeds
plot_compare_evokeds([red, blue], picks=3, truncate_yaxis=True)
plot_compare_evokeds([[red, evoked], [blue, evoked]],
picks=3) # list of lists
# test picking & plotting grads
contrast = dict()
contrast["red/stim"] = list((evoked.copy(), red))
contrast["blue/stim"] = list((evoked.copy(), blue))
# test a bunch of params at once
for evokeds_ in (evoked.copy().pick_types(meg='mag'), contrast,
[red, blue], [[red, evoked], [blue, evoked]]):
plot_compare_evokeds(evokeds_, picks=0, ci=True) # also tests CI
plt.close('all')
# test styling + a bunch of other params at once
colors, linestyles = dict(red='r', blue='b'), dict(red='--', blue='-')
plot_compare_evokeds(contrast, colors=colors, linestyles=linestyles,
picks=[0, 2], vlines=[.01, -.04], invert_y=True,
truncate_yaxis=False, ylim=dict(mag=(-10, 10)),
styles={"red/stim": {"linewidth": 1}},
show_sensors=True)
# various bad styles
params = [dict(picks=3, colors=dict(fake=1)),
dict(picks=3, styles=dict(fake=1)), dict(picks=3, gfp=True),
dict(picks=3, show_sensors="a"),
dict(colors=dict(red=10., blue=-2))]
for param in params:
assert_raises(ValueError, plot_compare_evokeds, evoked, **param)
assert_raises(TypeError, plot_compare_evokeds, evoked, picks='str')
assert_raises(TypeError, plot_compare_evokeds, evoked, vlines='x')
plt.close('all')
# `evoked` must contain Evokeds
assert_raises(TypeError, plot_compare_evokeds, [[1, 2], [3, 4]])
# `ci` must be float or None
assert_raises(TypeError, plot_compare_evokeds, contrast, ci='err')
# test all-positive ylim
contrast["red/stim"], contrast["blue/stim"] = red, blue
plot_compare_evokeds(contrast, picks=[0], colors=['r', 'b'],
ylim=dict(mag=(1, 10)), ci=_parametric_ci,
truncate_yaxis='max_ticks', show_sensors=False,
show_legend=False)
# sequential colors
evokeds = (evoked, blue, red)
contrasts = {"a{}/b".format(ii): ev for ii, ev in
enumerate(evokeds)}
colors = {"a" + str(ii): ii for ii, _ in enumerate(evokeds)}
contrasts["a1/c"] = evoked.copy()
for split in (True, False):
for linestyles in (["-"], {"b": "-", "c": ":"}):
plot_compare_evokeds(
contrasts, colors=colors, picks=[0], cmap='Reds',
split_legend=split, linestyles=linestyles,
ci=False, show_sensors=False)
colors = {"a" + str(ii): ii / len(evokeds)
for ii, _ in enumerate(evokeds)}
plot_compare_evokeds(
contrasts, colors=colors, picks=[0], cmap='Reds',
split_legend=split, linestyles=linestyles, ci=False,
show_sensors=False)
red.info["chs"][0]["loc"][:2] = 0 # test plotting channel at zero
plot_compare_evokeds(red, picks=[0],
ci=lambda x: [x.std(axis=0), -x.std(axis=0)])
plot_compare_evokeds([red, blue], picks=[0], cmap="summer", ci=None,
split_legend=None)
plot_compare_evokeds([red, blue], cmap=None, split_legend=True)
assert_raises(ValueError, plot_compare_evokeds, [red] * 20)
assert_raises(ValueError, plot_compare_evokeds, contrasts,
cmap='summer')
plt.close('all')
# Hack to test plotting of maxfiltered data
evoked_sss = evoked.copy()
sss = dict(sss_info=dict(in_order=80, components=np.arange(80)))
evoked_sss.info['proc_history'] = [dict(max_info=sss)]
evoked_sss.plot_white(cov, rank={'meg': 64}, time_unit='s')
assert_raises(
ValueError, evoked_sss.plot_white, cov, rank={'grad': 201},
time_unit='s')
evoked_sss.plot_white(cov, time_unit='s')
# plot with bad channels excluded, spatial_colors, zorder & pos. layout
evoked.rename_channels({'MEG 0133': 'MEG 0000'})
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True,
zorder='std', time_unit='s')
evoked.plot(exclude=[], spatial_colors=True, zorder='unsorted',
time_unit='s')
assert_raises(TypeError, evoked.plot, zorder='asdf', time_unit='s')
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
plt.close('all')
evoked.pick_channels(evoked.ch_names[:4])
with catch_logging() as log_file:
evoked.plot(verbose=True, time_unit='s')
assert_true('Need more than one' in log_file.getvalue())
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_evoked():
"""Test plotting of evoked."""
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
with warnings.catch_warnings(record=True):
fig = evoked.plot(proj=True, hline=[1], exclude=[], window_title='foo')
# 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 & spatial_colors & zorder
evoked.plot(exclude='bads')
# test selective updating of dict keys is working.
evoked.plot(hline=[1], units=dict(mag='femto foo'))
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
evoked_delayed_ssp.plot(proj='interactive')
evoked_delayed_ssp.apply_proj()
assert_raises(RuntimeError,
evoked_delayed_ssp.plot,
proj='interactive')
evoked_delayed_ssp.info['projs'] = []
assert_raises(RuntimeError,
evoked_delayed_ssp.plot,
proj='interactive')
assert_raises(RuntimeError,
evoked_delayed_ssp.plot,
proj='interactive',
axes='foo')
plt.close('all')
# test GFP only
evoked.plot(gfp='only')
assert_raises(ValueError, evoked.plot, gfp='foo')
evoked.plot_image(proj=True)
# plot with bad channels excluded
evoked.plot_image(exclude='bads', cmap='interactive')
evoked.plot_image(exclude=evoked.info['bads']) # does the same thing
plt.close('all')
evoked.plot_topo() # should auto-find layout
_line_plot_onselect(0, 200, ['mag', 'grad'], evoked.info, evoked.data,
evoked.times)
plt.close('all')
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
# test rank param.
evoked.plot_white(cov, rank={'mag': 101, 'grad': 201})
evoked.plot_white(cov, rank={'mag': 101}) # test rank param.
evoked.plot_white(cov, rank={'grad': 201}) # test rank param.
assert_raises(ValueError,
evoked.plot_white,
cov,
rank={
'mag': 101,
'grad': 201,
'meg': 306
})
assert_raises(ValueError, evoked.plot_white, cov, rank={'meg': 306})
evoked.plot_white([cov, cov])
# plot_compare_evokeds: test condition contrast, CI, color assignment
plot_compare_evokeds(evoked.copy().pick_types(meg='mag'))
plot_compare_evokeds(evoked.copy().pick_types(meg='grad'),
picks=[1, 2])
evoked.rename_channels({'MEG 2142': "MEG 1642"})
assert len(plot_compare_evokeds(evoked)) == 2
colors = dict(red='r', blue='b')
linestyles = dict(red='--', blue='-')
red, blue = evoked.copy(), evoked.copy()
red.data *= 1.1
blue.data *= 0.9
plot_compare_evokeds([red, blue], picks=3) # list of evokeds
plot_compare_evokeds([red, blue], picks=3, truncate_yaxis=True)
plot_compare_evokeds([[red, evoked], [blue, evoked]],
picks=3) # list of lists
# test picking & plotting grads
contrast = dict()
contrast["red/stim"] = list((evoked.copy(), red))
contrast["blue/stim"] = list((evoked.copy(), blue))
# test a bunch of params at once
plot_compare_evokeds(contrast,
colors=colors,
linestyles=linestyles,
picks=[0, 2],
vlines=[.01, -.04],
invert_y=True,
truncate_yaxis=False,
ylim=dict(mag=(-10, 10)),
styles={"red/stim": {
"linewidth": 1
}},
show_sensors=True)
assert_raises(ValueError, plot_compare_evokeds, contrast,
picks='str') # bad picks: not int
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
colors=dict(fake=1)) # 'fake' not in conds
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
styles=dict(fake=1)) # 'fake' not in conds
assert_raises(ValueError,
plot_compare_evokeds, [[1, 2], [3, 4]],
picks=3) # evoked must contain Evokeds
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
styles=dict(err=1)) # bad styles dict
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
gfp=True) # no single-channel GFP
assert_raises(TypeError,
plot_compare_evokeds,
evoked,
picks=3,
ci='fake') # ci must be float or None
assert_raises(TypeError,
plot_compare_evokeds,
evoked,
picks=3,
show_sensors='a') # show_sensors must be int or bool
contrast["red/stim"] = red
contrast["blue/stim"] = blue
plot_compare_evokeds(contrast,
picks=[0],
colors=['r', 'b'],
ylim=dict(mag=(1, 10)),
ci=_parametric_ci,
truncate_yaxis='max_ticks')
red.info["chs"][0]["loc"][:2] = 0 # test plotting channel at zero
plot_compare_evokeds(red, picks=[0])
# Hack to test plotting of maxfiltered data
evoked_sss = evoked.copy()
sss = dict(sss_info=dict(in_order=80, components=np.arange(80)))
evoked_sss.info['proc_history'] = [dict(max_info=sss)]
evoked_sss.plot_white(cov, rank={'meg': 64})
assert_raises(ValueError,
evoked_sss.plot_white,
cov,
rank={'grad': 201})
evoked_sss.plot_white(cov_fname)
# plot with bad channels excluded, spatial_colors, zorder & pos. layout
evoked.rename_channels({'MEG 0133': 'MEG 0000'})
evoked.plot(exclude=evoked.info['bads'],
spatial_colors=True,
gfp=True,
zorder='std')
evoked.plot(exclude=[], spatial_colors=True, zorder='unsorted')
assert_raises(TypeError, evoked.plot, zorder='asdf')
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
plt.close('all')
evoked.pick_channels(evoked.ch_names[:4])
with catch_logging() as log_file:
evoked.plot(verbose=True)
assert_true('Need more than one' in log_file.getvalue())
def test_plot_evoked():
"""Test plotting of evoked."""
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
with warnings.catch_warnings(record=True):
fig = evoked.plot(proj=True, hline=[1], exclude=[], window_title='foo')
# 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 & spatial_colors & zorder
evoked.plot(exclude='bads')
# test selective updating of dict keys is working.
evoked.plot(hline=[1], units=dict(mag='femto foo'))
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
evoked_delayed_ssp.plot(proj='interactive')
evoked_delayed_ssp.apply_proj()
assert_raises(RuntimeError,
evoked_delayed_ssp.plot,
proj='interactive')
evoked_delayed_ssp.info['projs'] = []
assert_raises(RuntimeError,
evoked_delayed_ssp.plot,
proj='interactive')
assert_raises(RuntimeError,
evoked_delayed_ssp.plot,
proj='interactive',
axes='foo')
plt.close('all')
# test GFP only
evoked.plot(gfp='only')
assert_raises(ValueError, evoked.plot, gfp='foo')
evoked.plot_image(proj=True)
# plot with bad channels excluded
evoked.plot_image(exclude='bads', cmap='interactive')
evoked.plot_image(exclude=evoked.info['bads']) # does the same thing
plt.close('all')
evoked.plot_topo() # should auto-find layout
_line_plot_onselect(0, 200, ['mag', 'grad'], evoked.info, evoked.data,
evoked.times)
plt.close('all')
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
evoked.plot_white(cov)
evoked.plot_white([cov, cov])
# plot_compare_evokeds: test condition contrast, CI, color assignment
plot_compare_evokeds(evoked.copy().pick_types(meg='mag'))
plot_compare_evokeds(evoked.copy().pick_types(meg='grad'),
picks=[1, 2])
evoked.rename_channels({'MEG 2142': "MEG 1642"})
assert len(plot_compare_evokeds(evoked)) == 2
colors = dict(red='r', blue='b')
linestyles = dict(red='--', blue='-')
red, blue = evoked.copy(), evoked.copy()
red.data *= 1.1
blue.data *= 0.9
plot_compare_evokeds([red, blue], picks=3) # list of evokeds
plot_compare_evokeds([red, blue], picks=3, truncate_yaxis=True)
plot_compare_evokeds([[red, evoked], [blue, evoked]],
picks=3) # list of lists
# test picking & plotting grads
contrast = dict()
contrast["red/stim"] = list((evoked.copy(), red))
contrast["blue/stim"] = list((evoked.copy(), blue))
# test a bunch of params at once
plot_compare_evokeds(contrast,
colors=colors,
linestyles=linestyles,
picks=[0, 2],
vlines=[.01, -.04],
invert_y=True,
truncate_yaxis=False,
ylim=dict(mag=(-10, 10)),
styles={"red/stim": {
"linewidth": 1
}})
assert_raises(ValueError, plot_compare_evokeds, contrast,
picks='str') # bad picks: not int
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
colors=dict(fake=1)) # 'fake' not in conds
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
styles=dict(fake=1)) # 'fake' not in conds
assert_raises(ValueError,
plot_compare_evokeds, [[1, 2], [3, 4]],
picks=3) # evoked must contain Evokeds
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
styles=dict(err=1)) # bad styles dict
assert_raises(ValueError,
plot_compare_evokeds,
evoked,
picks=3,
gfp=True) # no single-channel GFP
assert_raises(TypeError,
plot_compare_evokeds,
evoked,
picks=3,
ci='fake') # ci must be float or None
contrast["red/stim"] = red
contrast["blue/stim"] = blue
plot_compare_evokeds(contrast,
picks=[0],
colors=['r', 'b'],
ylim=dict(mag=(1, 10)),
ci=_parametric_ci)
# isolated test of CI functions
arr = np.linspace(0, 1, 1000)[..., np.newaxis]
assert_allclose(_ci(arr, method="parametric"),
_ci(arr, method="bootstrap"),
rtol=.005)
assert_allclose(_bootstrap_ci(arr, statfun="median", random_state=0),
_bootstrap_ci(arr, statfun="mean", random_state=0),
rtol=.1)
# Hack to test plotting of maxfiltered data
evoked_sss = evoked.copy()
evoked_sss.info['proc_history'] = [dict(max_info=None)]
evoked_sss.plot_white(cov)
evoked_sss.plot_white(cov_fname)
# plot with bad channels excluded, spatial_colors, zorder & pos. layout
evoked.rename_channels({'MEG 0133': 'MEG 0000'})
evoked.plot(exclude=evoked.info['bads'],
spatial_colors=True,
gfp=True,
zorder='std')
evoked.plot(exclude=[], spatial_colors=True, zorder='unsorted')
assert_raises(TypeError, evoked.plot, zorder='asdf')
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
plt.close('all')
evoked.pick_channels(evoked.ch_names[:4])
with warnings.catch_warnings(record=True) as ws:
evoked.plot()
assert_equal(len(ws), 2)
assert_true(all('Need more than one' in str(w.message) for w in ws))
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')
def test_plot_evoked():
"""Test plotting of evoked."""
import matplotlib.pyplot as plt
rng = np.random.RandomState(0)
evoked = _get_epochs().average()
fig = evoked.plot(proj=True, hline=[1], exclude=[], window_title='foo',
time_unit='s')
# 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 & spatial_colors & zorder
evoked.plot(exclude='bads', time_unit='s')
# test selective updating of dict keys is working.
evoked.plot(hline=[1], units=dict(mag='femto foo'), time_unit='s')
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
evoked_delayed_ssp.plot(proj='interactive', time_unit='s')
evoked_delayed_ssp.apply_proj()
pytest.raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', time_unit='s')
evoked_delayed_ssp.info['projs'] = []
pytest.raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', time_unit='s')
pytest.raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', axes='foo', time_unit='s')
plt.close('all')
# test GFP only
evoked.plot(gfp='only', time_unit='s')
pytest.raises(ValueError, evoked.plot, gfp='foo', time_unit='s')
evoked.plot_image(proj=True, time_unit='ms')
# fail nicely on NaN
evoked_nan = evoked.copy()
evoked_nan.data[:, 0] = np.nan
pytest.raises(ValueError, evoked_nan.plot)
with np.errstate(invalid='ignore'):
pytest.raises(ValueError, evoked_nan.plot_image)
pytest.raises(ValueError, evoked_nan.plot_joint)
# test mask
evoked.plot_image(picks=[1, 2], mask=evoked.data > 0, time_unit='s')
evoked.plot_image(picks=[1, 2], mask_cmap=None, colorbar=False,
mask=np.ones(evoked.data.shape).astype(bool),
time_unit='s')
with pytest.warns(RuntimeWarning, match='not adding contour'):
evoked.plot_image(picks=[1, 2], mask=None, mask_style="both",
time_unit='s')
pytest.raises(ValueError, evoked.plot_image, mask=evoked.data[1:, 1:] > 0,
time_unit='s')
# plot with bad channels excluded
evoked.plot_image(exclude='bads', cmap='interactive', time_unit='s')
evoked.plot_image(exclude=evoked.info['bads'], time_unit='s') # same thing
plt.close('all')
pytest.raises(ValueError, evoked.plot_image, picks=[0, 0],
time_unit='s') # duplicates
ch_names = ["MEG 1131", "MEG 0111"]
picks = [evoked.ch_names.index(ch) for ch in ch_names]
evoked.plot_image(show_names="all", time_unit='s', picks=picks)
yticklabels = plt.gca().get_yticklabels()
for tick_target, tick_observed in zip(ch_names, yticklabels):
assert tick_target in str(tick_observed)
evoked.plot_image(show_names=True, time_unit='s')
# test groupby
evoked.plot_image(group_by=dict(sel=[0, 7]), axes=dict(sel=plt.axes()))
plt.close('all')
for group_by, axes in (("something", dict()), (dict(), "something")):
pytest.raises(ValueError, evoked.plot_image, group_by=group_by,
axes=axes)
# 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')
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
cov['projs'] = [] # avoid warnings
# test rank param.
evoked.plot_white(cov, rank={'mag': 101, 'grad': 201}, time_unit='s')
evoked.plot_white(cov, rank={'mag': 101}, time_unit='s') # test rank param
evoked.plot_white(cov, rank={'grad': 201}, time_unit='s')
pytest.raises(
ValueError, evoked.plot_white, cov,
rank={'mag': 101, 'grad': 201, 'meg': 306}, time_unit='s')
pytest.raises(
ValueError, evoked.plot_white, cov, rank={'meg': 306}, time_unit='s')
evoked.plot_white([cov, cov], time_unit='s')
# plot_compare_evokeds: test condition contrast, CI, color assignment
plot_compare_evokeds(evoked.copy().pick_types(meg='mag'))
plot_compare_evokeds(
evoked.copy().pick_types(meg='grad'), picks=[1, 2],
show_sensors="upper right", show_legend="upper left")
evokeds = [evoked.copy() for _ in range(10)]
for evoked in evokeds:
evoked.data += (rng.randn(*evoked.data.shape) *
np.std(evoked.data, axis=-1, keepdims=True))
for picks in ([0], [1], [2], [0, 2], [1, 2], [0, 1, 2],):
figs = plot_compare_evokeds([evokeds], picks=picks, ci=0.95)
if not isinstance(figs, list):
figs = [figs]
for fig in figs:
ext = fig.axes[0].collections[0].get_paths()[0].get_extents()
xs, ylim = ext.get_points().T
assert_allclose(xs, evoked.times[[0, -1]])
line = fig.axes[0].lines[0]
xs = line.get_xdata()
assert_allclose(xs, evoked.times)
ys = line.get_ydata()
assert (ys < ylim[1]).all()
assert (ys > ylim[0]).all()
plt.close('all')
evoked.rename_channels({'MEG 2142': "MEG 1642"})
assert len(plot_compare_evokeds(evoked)) == 2
colors = dict(red='r', blue='b')
linestyles = dict(red='--', blue='-')
red, blue = evoked.copy(), evoked.copy()
red.data *= 1.1
blue.data *= 0.9
plot_compare_evokeds([red, blue], picks=3) # list of evokeds
plot_compare_evokeds([red, blue], picks=3, truncate_yaxis=True,
vlines=[]) # also testing empty vlines here
plot_compare_evokeds([[red, evoked], [blue, evoked]],
picks=3) # list of lists
# test picking & plotting grads
contrast = dict()
contrast["red/stim"] = list((evoked.copy(), red))
contrast["blue/stim"] = list((evoked.copy(), blue))
# test a bunch of params at once
for evokeds_ in (evoked.copy().pick_types(meg='mag'), contrast,
[red, blue], [[red, evoked], [blue, evoked]]):
plot_compare_evokeds(evokeds_, picks=0, ci=True) # also tests CI
plt.close('all')
# test styling + a bunch of other params at once
colors, linestyles = dict(red='r', blue='b'), dict(red='--', blue='-')
plot_compare_evokeds(contrast, colors=colors, linestyles=linestyles,
picks=[0, 2], vlines=[.01, -.04], invert_y=True,
truncate_yaxis=False, ylim=dict(mag=(-10, 10)),
styles={"red/stim": {"linewidth": 1}},
show_sensors=True)
# various bad styles
params = [dict(picks=3, colors=dict(fake=1)),
dict(picks=3, styles=dict(fake=1)), dict(picks=3, gfp=True),
dict(picks=3, show_sensors="a"),
dict(colors=dict(red=10., blue=-2))]
for param in params:
pytest.raises(ValueError, plot_compare_evokeds, evoked, **param)
pytest.raises(TypeError, plot_compare_evokeds, evoked, picks='str')
pytest.raises(TypeError, plot_compare_evokeds, evoked, vlines='x')
plt.close('all')
# `evoked` must contain Evokeds
pytest.raises(TypeError, plot_compare_evokeds, [[1, 2], [3, 4]])
# `ci` must be float or None
pytest.raises(TypeError, plot_compare_evokeds, contrast, ci='err')
# test all-positive ylim
contrast["red/stim"], contrast["blue/stim"] = red, blue
plot_compare_evokeds(contrast, picks=[0], colors=['r', 'b'],
ylim=dict(mag=(1, 10)), ci=_parametric_ci,
truncate_yaxis='max_ticks', show_sensors=False,
show_legend=False)
# sequential colors
evokeds = (evoked, blue, red)
contrasts = {"a{}/b".format(ii): ev for ii, ev in
enumerate(evokeds)}
colors = {"a" + str(ii): ii for ii, _ in enumerate(evokeds)}
contrasts["a1/c"] = evoked.copy()
for split in (True, False):
for linestyles in (["-"], {"b": "-", "c": ":"}):
plot_compare_evokeds(
contrasts, colors=colors, picks=[0], cmap='Reds',
split_legend=split, linestyles=linestyles,
ci=False, show_sensors=False)
colors = {"a" + str(ii): ii / len(evokeds)
for ii, _ in enumerate(evokeds)}
plot_compare_evokeds(
contrasts, colors=colors, picks=[0], cmap='Reds',
split_legend=split, linestyles=linestyles, ci=False,
show_sensors=False)
red.info["chs"][0]["loc"][:2] = 0 # test plotting channel at zero
plot_compare_evokeds(red, picks=[0],
ci=lambda x: [x.std(axis=0), -x.std(axis=0)])
plot_compare_evokeds([red, blue], picks=[0], cmap="summer", ci=None,
split_legend=None)
plot_compare_evokeds([red, blue], cmap=None, split_legend=True)
pytest.raises(ValueError, plot_compare_evokeds, [red] * 20)
pytest.raises(ValueError, plot_compare_evokeds, contrasts,
cmap='summer')
plt.close('all')
# Hack to test plotting of maxfiltered data
evoked_sss = evoked.copy()
sss = dict(sss_info=dict(in_order=80, components=np.arange(80)))
evoked_sss.info['proc_history'] = [dict(max_info=sss)]
evoked_sss.plot_white(cov, rank={'meg': 64}, time_unit='s')
pytest.raises(
ValueError, evoked_sss.plot_white, cov, rank={'grad': 201},
time_unit='s')
evoked_sss.plot_white(cov, time_unit='s')
# plot with bad channels excluded, spatial_colors, zorder & pos. layout
evoked.rename_channels({'MEG 0133': 'MEG 0000'})
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True,
zorder='std', time_unit='s')
evoked.plot(exclude=[], spatial_colors=True, zorder='unsorted',
time_unit='s')
pytest.raises(TypeError, evoked.plot, zorder='asdf', time_unit='s')
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
plt.close('all')
evoked.pick_channels(evoked.ch_names[:4])
with catch_logging() as log_file:
evoked.plot(verbose=True, time_unit='s')
assert 'Need more than one' in log_file.getvalue()
def test_plot_evoked():
"""Test plotting of evoked."""
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
with warnings.catch_warnings(record=True):
fig = evoked.plot(proj=True, hline=[1], exclude=[], window_title='foo')
# 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 & spatial_colors & zorder
evoked.plot(exclude='bads')
# test selective updating of dict keys is working.
evoked.plot(hline=[1], units=dict(mag='femto foo'))
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
evoked_delayed_ssp.plot(proj='interactive')
evoked_delayed_ssp.apply_proj()
assert_raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive')
evoked_delayed_ssp.info['projs'] = []
assert_raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive')
assert_raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', axes='foo')
plt.close('all')
# test GFP only
evoked.plot(gfp='only')
assert_raises(ValueError, evoked.plot, gfp='foo')
evoked.plot_image(proj=True)
# plot with bad channels excluded
evoked.plot_image(exclude='bads', cmap='interactive')
evoked.plot_image(exclude=evoked.info['bads']) # does the same thing
plt.close('all')
evoked.plot_topo() # should auto-find layout
_line_plot_onselect(0, 200, ['mag', 'grad'], evoked.info, evoked.data,
evoked.times)
plt.close('all')
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
evoked.plot_white(cov)
evoked.plot_white([cov, cov])
# plot_compare_evokeds: test condition contrast, CI, color assignment
plot_compare_evokeds(evoked.copy().pick_types(meg='mag'))
evoked.rename_channels({'MEG 2142': "MEG 1642"})
assert len(plot_compare_evokeds(evoked)) == 2
colors = dict(red='r', blue='b')
linestyles = dict(red='--', blue='-')
red, blue = evoked.copy(), evoked.copy()
red.data *= 1.1
blue.data *= 0.9
plot_compare_evokeds([red, blue], picks=3) # list of evokeds
plot_compare_evokeds([[red, evoked], [blue, evoked]],
picks=3) # list of lists
# test picking & plotting grads
contrast = dict()
contrast["red/stim"] = list((evoked.copy(), red))
contrast["blue/stim"] = list((evoked.copy(), blue))
# test a bunch of params at once
plot_compare_evokeds(contrast, colors=colors, linestyles=linestyles,
picks=[0, 2], vlines=[.01, -.04], invert_y=True,
truncate_yaxis=False, ylim=dict(mag=(-10, 10)),
styles={"red/stim": {"linewidth": 1}})
assert_raises(ValueError, plot_compare_evokeds,
contrast, picks='str') # bad picks: not int
assert_raises(ValueError, plot_compare_evokeds, evoked, picks=3,
colors=dict(fake=1)) # 'fake' not in conds
assert_raises(ValueError, plot_compare_evokeds, evoked, picks=3,
styles=dict(fake=1)) # 'fake' not in conds
assert_raises(ValueError, plot_compare_evokeds, [[1, 2], [3, 4]],
picks=3) # evoked must contain Evokeds
assert_raises(ValueError, plot_compare_evokeds, evoked, picks=3,
styles=dict(err=1)) # bad styles dict
assert_raises(ValueError, plot_compare_evokeds, evoked, picks=3,
gfp=True) # no single-channel GFP
assert_raises(TypeError, plot_compare_evokeds, evoked, picks=3,
ci='fake') # ci must be float or None
contrast["red/stim"] = red
contrast["blue/stim"] = blue
plot_compare_evokeds(contrast, picks=[0], colors=['r', 'b'],
ylim=dict(mag=(1, 10)))
# Hack to test plotting of maxfiltered data
evoked_sss = evoked.copy()
evoked_sss.info['proc_history'] = [dict(max_info=None)]
evoked_sss.plot_white(cov)
evoked_sss.plot_white(cov_fname)
# plot with bad channels excluded, spatial_colors, zorder & pos. layout
evoked.rename_channels({'MEG 0133': 'MEG 0000'})
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True,
zorder='std')
evoked.plot(exclude=[], spatial_colors=True, zorder='unsorted')
assert_raises(TypeError, evoked.plot, zorder='asdf')
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
plt.close('all')
