def test_plot_topomap_basic(monkeypatch):
"""Test basics of topomap plotting."""
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
res = 8
fast_test = dict(res=res, contours=0, sensors=False, time_unit='s')
fast_test_noscale = dict(res=res, contours=0, sensors=False)
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
plt_topomap = partial(ev_bad.plot_topomap, **fast_test)
plt_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
pytest.raises(ValueError, plt_topomap, ch_type='mag')
pytest.raises(ValueError, plt_topomap, times=[-100]) # bad time
pytest.raises(ValueError, plt_topomap, times=[[0]]) # bad time
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms')
# extrapolation to the edges of the convex hull or the head circle
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='local')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head',
outlines='skirt')
# extrapolation options when < 4 channels:
temp_data = np.random.random(3)
picks = channel_indices_by_type(evoked.info)['mag'][:3]
info_sel = pick_info(evoked.info, picks)
plot_topomap(temp_data, info_sel, extrapolate='local', res=res)
plot_topomap(temp_data, info_sel, extrapolate='head', res=res)
# make sure extrapolation works for 3 channels with border='mean'
# (if extra points are placed incorrectly some of them have only
# other extra points as neighbours and border='mean' fails)
plot_topomap(temp_data,
info_sel,
extrapolate='local',
border='mean',
res=res)
# border=0 and border='mean':
# ---------------------------
ch_pos = np.array(
sum(([[0, 0, r], [r, 0, 0], [-r, 0, 0], [0, -r, 0], [0, r, 0]]
for r in np.linspace(0.2, 1.0, 5)), []))
rng = np.random.RandomState(23)
data = np.full(len(ch_pos), 5) + rng.randn(len(ch_pos))
info = create_info(len(ch_pos), 250, 'eeg')
ch_pos_dict = {name: pos for name, pos in zip(info['ch_names'], ch_pos)}
dig = make_dig_montage(ch_pos_dict, coord_frame='head')
info.set_montage(dig)
# border=0
ax, _ = plot_topomap(data, info, extrapolate='head', border=0, sphere=1)
img_data = ax.get_array().data
assert np.abs(img_data[31, 31] - data[0]) < 0.12
assert np.abs(img_data[0, 0]) < 1.5
# border='mean'
ax, _ = plot_topomap(data,
info,
extrapolate='head',
border='mean',
sphere=1)
img_data = ax.get_array().data
assert np.abs(img_data[31, 31] - data[0]) < 0.12
assert img_data[0, 0] > 5
# error when not numeric or str:
error_msg = 'border must be an instance of numeric or str'
with pytest.raises(TypeError, match=error_msg):
plot_topomap(data, info, extrapolate='head', border=[1, 2, 3])
# error when str is not 'mean':
error_msg = "The only allowed value is 'mean', but got 'fancy' instead."
with pytest.raises(ValueError, match=error_msg):
plot_topomap(data, info, extrapolate='head', border='fancy')
# test channel placement when only 'grad' are picked:
# ---------------------------------------------------
info_grad = evoked.copy().pick('grad').info
n_grads = len(info_grad['ch_names'])
data = np.random.randn(n_grads)
img, _ = plot_topomap(data, info_grad)
# check that channels are scattered around x == 0
pos = img.axes.collections[-1].get_offsets()
prop_channels_on_the_right = (pos[:, 0] > 0).mean()
assert prop_channels_on_the_right < 0.6
# other:
# ------
plt_topomap = partial(evoked.plot_topomap, **fast_test)
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
plt_topomap(axes=axes, colorbar=False)
plt.close('all')
plt_topomap(times=[-0.1, 0.2])
plt.close('all')
evoked_grad = evoked.copy().crop(0, 0).pick_types(meg='grad')
mask = np.zeros((204, 1), bool)
mask[[0, 3, 5, 6]] = True
names = []
def proc_names(x):
names.append(x)
return x[4:]
evoked_grad.plot_topomap(ch_type='grad',
times=[0],
mask=mask,
show_names=proc_names,
**fast_test)
assert_equal(sorted(names),
['MEG 011x', 'MEG 012x', 'MEG 013x', 'MEG 014x'])
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
plt_topomap(ch_type='mag', outlines=None)
times = [0.1]
plt_topomap(times, ch_type='grad', mask=mask)
plt_topomap(times, ch_type='planar1')
plt_topomap(times, ch_type='planar2')
plt_topomap(times,
ch_type='grad',
mask=mask,
show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
with pytest.raises(ValueError, match='number of seconds; got -'):
plt_topomap(times, ch_type='eeg', average=-1e3)
with pytest.raises(TypeError, match='number of seconds; got type'):
plt_topomap(times, ch_type='eeg', average='x')
p = plt_topomap(times,
ch_type='grad',
image_interp='bilinear',
show_names=lambda x: x.replace('MEG', ''))
subplot = [x for x in p.get_children() if 'Subplot' in str(type(x))]
assert len(subplot) >= 1, [type(x) for x in p.get_children()]
subplot = subplot[0]
have_all = all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text))
assert have_all
# Plot array
for ch_type in ('mag', 'grad'):
evoked_ = evoked.copy().pick_types(eeg=False, meg=ch_type)
plot_topomap(evoked_.data[:, 0], evoked_.info, **fast_test_noscale)
# fail with multiple channel types
pytest.raises(ValueError, plot_topomap, evoked.data[0, :], evoked.info)
# Test title
def get_texts(p):
return [
x.get_text() for x in p.get_children()
if isinstance(x, matplotlib.text.Text)
]
p = plt_topomap(times, ch_type='eeg', average=0.01)
assert_equal(len(get_texts(p)), 0)
p = plt_topomap(times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
plt_topomap(times, ch_type='mag')
# projs have already been applied
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive',
time_unit='s')
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
fig1 = evoked.plot_topomap('interactive',
'mag',
proj='interactive',
**fast_test)
_fake_click(fig1, fig1.axes[1], (0.5, 0.5)) # click slider
data_max = np.max(fig1.axes[0].images[0]._A)
fig2 = plt.gcf()
_fake_click(fig2, fig2.axes[0], (0.075, 0.775)) # toggle projector
# make sure projector gets toggled
assert (np.max(fig1.axes[0].images[0]._A) != data_max)
with monkeypatch.context() as m: # speed it up by not actually plotting
m.setattr(topomap, '_plot_topomap', lambda *args, **kwargs:
(None, None, None))
with pytest.warns(RuntimeWarning, match='More than 25 topomaps plots'):
plot_evoked_topomap(evoked, [0.1] * 26, colorbar=False)
pytest.raises(ValueError,
plot_evoked_topomap,
evoked, [-3e12, 15e6],
time_unit='s')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
eeg_picks = pick_types(evoked.info, meg=False, eeg=True)
pos, outlines = _get_pos_outlines(evoked.info, eeg_picks, 0.1)
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines, **fast_test)
plt.close('all')
# Test interactive cmap
fig = plot_evoked_topomap(evoked,
times=[0., 0.1],
ch_type='eeg',
cmap=('Reds', True),
title='title',
**fast_test)
fig.canvas.key_press_event('up')
fig.canvas.key_press_event(' ')
fig.canvas.key_press_event('down')
cbar = fig.get_axes()[0].CB # Fake dragging with mouse.
ax = cbar.cbar.ax
_fake_click(fig, ax, (0.1, 0.1))
_fake_click(fig, ax, (0.1, 0.2), kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
_fake_click(fig, ax, (0.1, 0.1), button=3)
_fake_click(fig, ax, (0.1, 0.2), button=3, kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down
fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687),
radius=.46,
clip_on=True,
transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked,
times,
ch_type='eeg',
outlines=outlines,
**fast_test)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg',
time_unit='s')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
pytest.raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
pytest.raises(ValueError, plot_topomap, data, pos_1d)
pytest.raises(ValueError, plot_topomap, data, pos_3d)
pytest.raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
pytest.raises(ValueError, plot_topomap, data, pos_x)
pytest.raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
evoked.plot_topomap(times='peaks', axes=axes, **fast_test)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
# Test excluding bads channels
evoked_grad.info['bads'] += [evoked_grad.info['ch_names'][0]]
orig_bads = evoked_grad.info['bads']
evoked_grad.plot_topomap(ch_type='grad', times=[0], time_unit='ms')
assert_array_equal(evoked_grad.info['bads'], orig_bads)
plt.close('all')
def test_plot_topomap_basic():
"""Test basics of topomap plotting."""
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
res = 8
fast_test = dict(res=res, contours=0, sensors=False, time_unit='s')
fast_test_noscale = dict(res=res, contours=0, sensors=False)
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
plt_topomap = partial(ev_bad.plot_topomap, **fast_test)
plt_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
pytest.raises(ValueError, plt_topomap, ch_type='mag')
with pytest.deprecated_call(match='head_pos'):
plt_topomap(head_pos='foo')
pytest.raises(ValueError, plt_topomap, times=[-100]) # bad time
pytest.raises(ValueError, plt_topomap, times=[[0]]) # bad time
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms')
# extrapolation to the edges of the convex hull or the head circle
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='local')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head',
outlines='skirt')
# extrapolation options when < 4 channels:
temp_data = np.random.random(3)
picks = channel_indices_by_type(evoked.info)['mag'][:3]
info_sel = pick_info(evoked.info, picks)
plot_topomap(temp_data, info_sel, extrapolate='local', res=res)
plot_topomap(temp_data, info_sel, extrapolate='head', res=res)
plt_topomap = partial(evoked.plot_topomap, **fast_test)
with pytest.deprecated_call(match='layout'):
plt_topomap(0.1, layout=layout, scalings=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
plt_topomap(axes=axes, colorbar=False)
plt.close('all')
plt_topomap(times=[-0.1, 0.2])
plt.close('all')
evoked_grad = evoked.copy().crop(0, 0).pick_types(meg='grad')
mask = np.zeros((204, 1), bool)
mask[[0, 3, 5, 6]] = True
names = []
def proc_names(x):
names.append(x)
return x[4:]
evoked_grad.plot_topomap(ch_type='grad',
times=[0],
mask=mask,
show_names=proc_names,
**fast_test)
assert_equal(sorted(names),
['MEG 011x', 'MEG 012x', 'MEG 013x', 'MEG 014x'])
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
plt_topomap(ch_type='mag', outlines=None)
times = [0.1]
plt_topomap(times, ch_type='grad', mask=mask)
plt_topomap(times, ch_type='planar1')
plt_topomap(times, ch_type='planar2')
plt_topomap(times,
ch_type='grad',
mask=mask,
show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
pytest.raises(ValueError, plt_topomap, times, ch_type='eeg', average=-1e3)
pytest.raises(ValueError, plt_topomap, times, ch_type='eeg', average='x')
p = plt_topomap(times,
ch_type='grad',
image_interp='bilinear',
show_names=lambda x: x.replace('MEG', ''))
subplot = [x for x in p.get_children() if 'Subplot' in str(type(x))]
assert len(subplot) >= 1, [type(x) for x in p.get_children()]
subplot = subplot[0]
have_all = all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text))
assert have_all
# Plot array
for ch_type in ('mag', 'grad'):
evoked_ = evoked.copy().pick_types(eeg=False, meg=ch_type)
plot_topomap(evoked_.data[:, 0], evoked_.info, **fast_test_noscale)
# fail with multiple channel types
pytest.raises(ValueError, plot_topomap, evoked.data[0, :], evoked.info)
# Test title
def get_texts(p):
return [
x.get_text() for x in p.get_children()
if isinstance(x, matplotlib.text.Text)
]
p = plt_topomap(times, ch_type='eeg', average=0.01)
assert_equal(len(get_texts(p)), 0)
p = plt_topomap(times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
plt_topomap(times, ch_type='mag', layout=None)
# projs have already been applied
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive',
time_unit='s')
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
fig1 = evoked.plot_topomap('interactive',
'mag',
proj='interactive',
**fast_test)
_fake_click(fig1, fig1.axes[1], (0.5, 0.5)) # click slider
data_max = np.max(fig1.axes[0].images[0]._A)
fig2 = plt.gcf()
_fake_click(fig2, fig2.axes[0], (0.075, 0.775)) # toggle projector
# make sure projector gets toggled
assert (np.max(fig1.axes[0].images[0]._A) != data_max)
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
np.repeat(.1, 50),
time_unit='s')
pytest.raises(ValueError,
plot_evoked_topomap,
evoked, [-3e12, 15e6],
time_unit='s')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
eeg_picks = pick_types(evoked.info, meg=False, eeg=True)
pos, outlines = _get_pos_outlines(evoked.info, eeg_picks, 0.1)
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines, **fast_test)
plt.close('all')
# Test interactive cmap
fig = plot_evoked_topomap(evoked,
times=[0., 0.1],
ch_type='eeg',
cmap=('Reds', True),
title='title',
**fast_test)
fig.canvas.key_press_event('up')
fig.canvas.key_press_event(' ')
fig.canvas.key_press_event('down')
cbar = fig.get_axes()[0].CB # Fake dragging with mouse.
ax = cbar.cbar.ax
_fake_click(fig, ax, (0.1, 0.1))
_fake_click(fig, ax, (0.1, 0.2), kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
_fake_click(fig, ax, (0.1, 0.1), button=3)
_fake_click(fig, ax, (0.1, 0.2), button=3, kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down
fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687),
radius=.46,
clip_on=True,
transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked,
times,
ch_type='eeg',
outlines=outlines,
**fast_test)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg',
time_unit='s')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
pytest.raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
pytest.raises(ValueError, plot_topomap, data, pos_1d)
pytest.raises(ValueError, plot_topomap, data, pos_3d)
pytest.raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
pytest.raises(ValueError, plot_topomap, data, pos_x)
pytest.raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
evoked.plot_topomap(times='peaks', axes=axes, **fast_test)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
# Test excluding bads channels
evoked_grad.info['bads'] += [evoked_grad.info['ch_names'][0]]
orig_bads = evoked_grad.info['bads']
evoked_grad.plot_topomap(ch_type='grad', times=[0], time_unit='ms')
assert_array_equal(evoked_grad.info['bads'], orig_bads)
plt.close('all')