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')
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True,
zorder='std')
# 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
_butterfly_onselect(0, 200, ['mag', 'grad'], evoked)
plt.close('all')
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
evoked.plot_white(cov)
evoked.plot_white([cov, cov])
# 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)
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
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
evoked.plot(exclude='bads')
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True)
# 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')
evoked.plot_image(exclude=evoked.info['bads']) # does the same thing
plt.close('all')
evoked.plot_topo() # should auto-find layout
_butterfly_onselect(0, 200, ['mag'], evoked) # test averaged topomap
plt.close('all')
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
evoked.plot_white(cov)
evoked.plot_white([cov, cov])
# 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)
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
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')
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True,
zorder='std')
# 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
_butterfly_onselect(0, 200, ['mag', 'grad'], evoked)
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)
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
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')
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True,
zorder='std')
# 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
_butterfly_onselect(0, 200, ['mag', 'grad'], evoked)
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)
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
plt.close('all')
