def _construct_topo_side(info, kwargs):
from mne.viz.topomap import _check_outlines, _find_topomap_coords
radius = 0.5
ll = np.linspace(0, 2 * np.pi, 101)
head_x = np.cos(ll) * radius
head_y = np.sin(ll) * radius
mask_outlines = np.c_[head_x, head_y]
below_zero = mask_outlines[:, 0] < 0
removed_len = below_zero.sum()
filling = np.zeros((removed_len, 2))
filling[:, 1] = np.linspace(0.5, -0.5, num=removed_len)
mask_outlines[below_zero, :] = filling
head_pos = dict(center=(0., 0.))
picks = range(len(info['ch_names']))
pos = _find_topomap_coords(info, picks=picks)
# TODO currently uses outlines='head', but should change later
pos, outlines = _check_outlines(pos, outlines='head', head_pos=head_pos)
outlines['mask_pos'] = (mask_outlines[:, 0], mask_outlines[:, 1])
kwargs.update(dict(outlines=outlines, head_pos=head_pos))
# scale pos to min - max of the circle
scale_x = 0.425 / pos[:, 0].max()
scale_y = 0.425 / np.abs(pos[:, 1]).max()
pos[:, 0] *= scale_x
pos[:, 1] *= scale_y
info = pos
return info, kwargs
def __init__(self, url=None):
super().__init__()
if url is None:
ws_url = "ws://localhost:7777"
else:
ws_url = url
self.setWindowTitle("Contact Impedance")
self.ws_imp = WS_Imp(contact_plot=self, url=ws_url)
self.timer_interval = 0.5
self.ch_label = list()
gs_kw = dict(width_ratios=[30,1], height_ratios=[1])
self.fig, (self.ax, self.ax2) = plt.subplots(1, 2, gridspec_kw=gs_kw)
self.canvas = FigureCanvas(self.fig)
self.pos = list(channel_dict_2D.values()) # get all X,Y values
self.ch_names_ = list(channel_dict_2D.keys()) # get all channel's names
self.pos, self.outlines = topomap._check_outlines(self.pos, 'head') # from mne.viz libs, normalize the pos
self.cm = plt.cm.get_cmap('RdYlGn_r')
self.norm = mpl.colors.Normalize(vmin=0, vmax=2000)
self.colorbar = ColorbarBase(self.ax2, cmap=self.cm, norm=self.norm, ticks=[0, 500, 1000, 1500, 2000])
self.colorbar.set_ticklabels(['0 KOhm','500', '1000', '1500', '2000'])
while len(self.ch_label) is 0:
pass
self.plt_idx = [self.ch_names_.index(name) for name in self.ch_label] # get the index of those required channels
self.ch_table = QtGui.QTableWidget(len(self.ch_label), 2, parent=self)
item = QtGui.QTableWidgetItem("Channel")
self.ch_table.setHorizontalHeaderItem (0, item)
item = QtGui.QTableWidgetItem("Impedance (KOhm)")
self.ch_table.setHorizontalHeaderItem (1, item)
self.draw(self.ch_label, [0]*len(self.ch_label))
header = self.ch_table.horizontalHeader()
header.setResizeMode(QtGui.QHeaderView.ResizeToContents)
header.setStretchLastSection(True)
hlayout = QtGui.QHBoxLayout(self)
hlayout.addWidget(self.canvas)
hlayout.addWidget(self.ch_table)
self.setup_signal_handler()
self.show()
self.value = 0
def init_ui(self):
self.cmap = mpl.cm.get_cmap('Dark2')
self.norm = mpl.colors.Normalize(vmin=0, vmax=8)
self.fig, (self.axes) = plt.subplots(len(self.ws_data.ch_label) + 2, 1)
self.colorbar = ColorbarBase(self.axes[-1],
cmap=self.cmap,
norm=self.norm,
ticks=[i + 0.5 for i in range(8)])
self.colorbar.set_ticklabels([
'Delta (1-3 Hz)', 'Theta (4-7 Hz)', 'Low Alpha (8-10 Hz)',
'High Alpha (11-12 Hz)', 'Low Beta (13-15 Hz)',
'Mid Beta (16-19 Hz)', 'High Beta (20-35 Hz)', 'Gamma (36-50 Hz)'
])
self.canvas = FigureCanvas(self.fig)
self.pos = list(channel_dict_2D.values()) # get all X,Y values
self.ch_names_ = list(
channel_dict_2D.keys()) # get all channel's names
self.pos, self.outlines = topomap._check_outlines(
self.pos, 'head') # from mne.viz libs, normalize the pos
topomap._draw_outlines(self.axes[0], self.outlines)
self.plt_idx = [
self.ch_names_.index(name) for name in self.ws_data.ch_label
] # get the index of those required channels
ch_pos = [self.pos[idx] * 5 / 6 for idx in self.plt_idx]
for idx, pos in enumerate(ch_pos):
pos += [0.47, 0.47]
self.axes[idx + 1].set_position(list(pos) + [0.06, 0.06])
self.axes[idx + 1].axis("off")
self.axes[idx + 1].grid(True, axis='y')
self.axes[0].set_position([0, 0, 1, 1])
self.axes[-1].set_position([0.80, 0.85, 0.03, 0.13])
self.resize(1000, 800)
hlayout = QtGui.QHBoxLayout(self)
hlayout.addWidget(self.canvas)
def _plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True,
res=64, axes=None, names=None, show_names=False, mask=None,
mask_params=None, outlines='head',
contours=6, image_interp='bilinear', show=True,
head_pos=None, onselect=None, extrapolate='box', border=0):
import matplotlib.pyplot as plt
from matplotlib.widgets import RectangleSelector
data = np.asarray(data)
if isinstance(pos, Info): # infer pos from Info object
picks = _pick_data_channels(pos) # pick only data channels
pos = pick_info(pos, picks)
# check if there is only 1 channel type, and n_chans matches the data
ch_type = {channel_type(pos, idx)
for idx, _ in enumerate(pos["chs"])}
info_help = ("Pick Info with e.g. mne.pick_info and "
"mne.io.pick.channel_indices_by_type.")
if len(ch_type) > 1:
raise ValueError("Multiple channel types in Info structure. "
+ info_help)
elif len(pos["chs"]) != data.shape[0]:
raise ValueError("Number of channels in the Info object and "
"the data array does not match. " + info_help)
else:
ch_type = ch_type.pop()
if any(type_ in ch_type for type_ in ('planar', 'grad')):
# deal with grad pairs
from mne.channels.layout import (_merge_grad_data, find_layout,
_pair_grad_sensors)
picks, pos = _pair_grad_sensors(pos, find_layout(pos))
data = _merge_grad_data(data[picks]).reshape(-1)
else:
picks = list(range(data.shape[0]))
pos = _find_topomap_coords(pos, picks=picks)
if data.ndim > 1:
raise ValueError("Data needs to be array of shape (n_sensors,); got "
"shape %s." % str(data.shape))
# Give a helpful error message for common mistakes regarding the position
# matrix.
pos_help = ("Electrode positions should be specified as a 2D array with "
"shape (n_channels, 2). Each row in this matrix contains the "
"(x, y) position of an electrode.")
if pos.ndim != 2:
error = ("{ndim}D array supplied as electrode positions, where a 2D "
"array was expected").format(ndim=pos.ndim)
raise ValueError(error + " " + pos_help)
elif pos.shape[1] == 3:
error = ("The supplied electrode positions matrix contains 3 columns. "
"Are you trying to specify XYZ coordinates? Perhaps the "
"mne.channels.create_eeg_layout function is useful for you.")
raise ValueError(error + " " + pos_help)
# No error is raised in case of pos.shape[1] == 4. In this case, it is
# assumed the position matrix contains both (x, y) and (width, height)
# values, such as Layout.pos.
elif pos.shape[1] == 1 or pos.shape[1] > 4:
raise ValueError(pos_help)
if len(data) != len(pos):
raise ValueError("Data and pos need to be of same length. Got data of "
"length %s, pos of length %s" % (len(data), len(pos)))
norm = min(data) >= 0
vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm)
if cmap is None:
cmap = 'Reds' if norm else 'RdBu_r'
pos, outlines = _check_outlines(pos, outlines, head_pos)
assert isinstance(outlines, dict)
ax = axes if axes else plt.gca()
_prepare_topomap(pos, ax)
_use_default_outlines = any(k.startswith('head') for k in outlines)
if _use_default_outlines:
# prepare masking
_autoshrink(outlines, pos, res)
mask_params = _handle_default('mask_params', mask_params)
# find mask limits
xlim = np.inf, -np.inf,
ylim = np.inf, -np.inf,
mask_ = np.c_[outlines['mask_pos']]
xmin, xmax = (np.min(np.r_[xlim[0], mask_[:, 0]]),
np.max(np.r_[xlim[1], mask_[:, 0]]))
ymin, ymax = (np.min(np.r_[ylim[0], mask_[:, 1]]),
np.max(np.r_[ylim[1], mask_[:, 1]]))
# interpolate the data, we multiply clip radius by 1.06 so that pixelated
# edges of the interpolated image would appear under the mask
head_radius = (None if extrapolate == 'local' else
outlines['clip_radius'][0] * 1.06)
xi = np.linspace(xmin, xmax, res)
yi = np.linspace(ymin, ymax, res)
Xi, Yi = np.meshgrid(xi, yi)
interp = _GridData(pos, extrapolate, head_radius, border).set_values(data)
Zi = interp.set_locations(Xi, Yi)()
# plot outline
patch_ = None
if 'patch' in outlines:
patch_ = outlines['patch']
patch_ = patch_() if callable(patch_) else patch_
patch_.set_clip_on(False)
ax.add_patch(patch_)
ax.set_transform(ax.transAxes)
ax.set_clip_path(patch_)
if _use_default_outlines:
from matplotlib import patches
patch_ = patches.Ellipse((0, 0),
2 * outlines['clip_radius'][0],
2 * outlines['clip_radius'][1],
clip_on=True,
transform=ax.transData)
# plot interpolated map
im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower',
aspect='equal', extent=(xmin, xmax, ymin, ymax),
interpolation=image_interp)
# This tackles an incomprehensible matplotlib bug if no contours are
# drawn. To avoid rescalings, we will always draw contours.
# But if no contours are desired we only draw one and make it invisible.
linewidth = mask_params['markeredgewidth']
no_contours = False
if isinstance(contours, (np.ndarray, list)):
pass # contours precomputed
elif contours == 0:
contours, no_contours = 1, True
if (Zi == Zi[0, 0]).all():
cont = None # can't make contours for constant-valued functions
else:
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore')
cont = ax.contour(Xi, Yi, Zi, contours, colors='k',
linewidths=linewidth / 2.)
if no_contours and cont is not None:
for col in cont.collections:
col.set_visible(False)
if patch_ is not None:
im.set_clip_path(patch_)
if cont is not None:
for col in cont.collections:
col.set_clip_path(patch_)
pos_x, pos_y = pos.T
if sensors is not False and mask is None:
_plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax)
elif sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
idx = np.where(~mask)[0]
_plot_sensors(pos_x[idx], pos_y[idx], sensors=sensors, ax=ax)
elif not sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
if isinstance(outlines, dict):
_draw_outlines(ax, outlines)
if show_names:
if names is None:
raise ValueError("To show names, a list of names must be provided"
" (see `names` keyword).")
if show_names is True:
def _show_names(x):
return x
else:
_show_names = show_names
show_idx = np.arange(len(names)) if mask is None else np.where(mask)[0]
for ii, (p, ch_id) in enumerate(zip(pos, names)):
if ii not in show_idx:
continue
ch_id = _show_names(ch_id)
ax.text(p[0], p[1], ch_id, horizontalalignment='center',
verticalalignment='center', size='x-small')
if onselect is not None:
ax.RS = RectangleSelector(ax, onselect=onselect)
plt_show(show)
return im, cont, interp, patch_
def test_plot_topomap():
"""Test topomap plotting."""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 8
fast_test = {"res": res, "contours": 0, "sensors": False}
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type='grad',
times=[0, 0.1],
butterfly=False)
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close('all')
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
assert_raises(ValueError, plt_topomap, ch_type='mag')
assert_raises(TypeError, plt_topomap, head_pos='foo')
assert_raises(KeyError, plt_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, plt_topomap, head_pos=dict(center=0))
assert_raises(ValueError, plt_topomap, times=[-100]) # bad time
assert_raises(ValueError, plt_topomap, times=[[0]]) # bad time
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100])
plt_topomap = partial(evoked.plot_topomap, **fast_test)
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')
assert_raises(ValueError, plt_topomap, times, ch_type='eeg', average=-1e3)
assert_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 isinstance(x, matplotlib.axes.Subplot)
][0]
assert_true(
all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# 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)
# fail with multiple channel types
assert_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
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
plt_topomap(times, ch_type='mag', layout=None)
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
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_true(np.max(fig1.axes[0].images[0]._A) != data_max)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
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]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(not outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos,
'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
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
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_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))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_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)]
with warnings.catch_warnings(record=True): # rightmost column
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])
assert_array_equal(evoked_grad.info['bads'], orig_bads)
plt.close('all')
def test_plot_topomap():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter("always")
res = 16
evoked = read_evokeds(evoked_fname, "Left Auditory", baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type="grad", times=[0, 0.1], butterfly=False)
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close("all")
ev_bad = evoked.pick_types(meg=False, eeg=True, copy=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type="mag")
assert_raises(TypeError, ev_bad.plot_topomap, head_pos="foo")
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo="bar"))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close("all")
axes = [plt.subplot(221), plt.subplot(222)]
evoked.plot_topomap(axes=axes, colorbar=False)
plt.close("all")
evoked.plot_topomap(times=[-0.1, 0.2])
plt.close("all")
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(ch_type="mag", outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type="eeg", res=res, scale=1)
evoked.plot_topomap(times, ch_type="grad", mask=mask, res=res)
evoked.plot_topomap(times, ch_type="planar1", res=res)
evoked.plot_topomap(times, ch_type="planar2", res=res)
evoked.plot_topomap(times, ch_type="grad", mask=mask, res=res, show_names=True, mask_params={"marker": "x"})
plt.close("all")
assert_raises(ValueError, evoked.plot_topomap, times, ch_type="eeg", res=res, average=-1000)
assert_raises(ValueError, evoked.plot_topomap, times, ch_type="eeg", res=res, average="hahahahah")
p = evoked.plot_topomap(
times, ch_type="grad", res=res, show_names=lambda x: x.replace("MEG", ""), image_interp="bilinear"
)
subplot = [x for x in p.get_children() if isinstance(x, matplotlib.axes.Subplot)][0]
assert_true(all("MEG" not in x.get_text() for x in subplot.get_children() if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if isinstance(x, matplotlib.text.Text)]
p = evoked.plot_topomap(times, ch_type="eeg", res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type="eeg", title="Custom", res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], "Custom")
plt.close("all")
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter("always")
evoked.plot_topomap(times, ch_type="mag", layout=None, res=res)
assert_raises(
RuntimeError, plot_evoked_topomap, evoked, 0.1, "mag", proj="interactive"
) # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname, "Left Auditory", baseline=(None, 0), proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter("always")
evoked.plot_topomap(0.1, "mag", proj="interactive", res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(0.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter("always")
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp["desc"].lower().find("eeg") < 0]
plot_projs_topomap(projs, res=res)
plt.close("all")
ax = plt.subplot(111)
plot_projs_topomap([projs[0]], res=res, axes=ax) # test axes param
plt.close("all")
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]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, "head")
assert_true("head" in outlines.keys())
assert_true("nose" in outlines.keys())
assert_true("ear_left" in outlines.keys())
assert_true("ear_right" in outlines.keys())
assert_true("autoshrink" in outlines.keys())
assert_true(outlines["autoshrink"])
assert_true("clip_radius" in outlines.keys())
assert_array_equal(outlines["clip_radius"], 0.5)
pos, outlines = _check_outlines(pos, "skirt")
assert_true("head" in outlines.keys())
assert_true("nose" in outlines.keys())
assert_true("ear_left" in outlines.keys())
assert_true("ear_right" in outlines.keys())
assert_true("autoshrink" in outlines.keys())
assert_true(not outlines["autoshrink"])
assert_true("clip_radius" in outlines.keys())
assert_array_equal(outlines["clip_radius"], 0.625)
pos, outlines = _check_outlines(pos, "skirt", head_pos={"scale": [1.2, 1.2]})
assert_array_equal(outlines["clip_radius"], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type="eeg", outlines="skirt")
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type="eeg", outlines=outlines)
plt.close("all")
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687), radius=0.46, clip_on=True, transform=plt.gca().transAxes)
outlines["patch"] = patch
plot_evoked_topomap(evoked, times, ch_type="eeg", outlines=outlines)
# Remove digitization points. Now topomap should fail
evoked.info["dig"] = None
assert_raises(RuntimeError, plot_evoked_topomap, evoked, times, ch_type="eeg")
plt.close("all")
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_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))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_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)]
with warnings.catch_warnings(record=True): # rightmost column
evoked.plot_topomap(times="peaks", axes=axes)
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])
def test_plot_topomap():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 16
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0))
ev_bad = evoked.pick_types(meg=False, eeg=True, copy=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, should plot EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type='mag')
assert_raises(TypeError, ev_bad.plot_topomap, head_pos='foo')
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close('all')
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(None, ch_type='mag', outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type='eeg', res=res, scale=1)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res)
evoked.plot_topomap(times, ch_type='planar1', res=res)
evoked.plot_topomap(times, ch_type='planar2', res=res)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res,
show_names=True, mask_params={'marker': 'x'})
plt.close('all')
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average=-1000)
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average='hahahahah')
p = evoked.plot_topomap(times, ch_type='grad', res=res,
show_names=lambda x: x.replace('MEG', ''),
image_interp='bilinear')
subplot = [x for x in p.get_children() if
isinstance(x, matplotlib.axes.Subplot)][0]
assert_true(all('MEG' not in x.get_text()
for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if
isinstance(x, matplotlib.text.Text)]
p = evoked.plot_topomap(times, ch_type='eeg', res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type='eeg', title='Custom', res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
evoked.plot_topomap(times, ch_type='mag', layout=None, res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0), proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
evoked.plot_topomap(0.1, 'mag', proj='interactive', res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter('always')
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs, res=res)
plt.close('all')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
if ch['eeg_loc'] is not None:
ch['eeg_loc'].fill(0)
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
pos = make_eeg_layout(evoked.info).pos
pos, outlines = _check_outlines(pos, 'head')
# test 1: pass custom outlines without patch
def patch():
return Circle((0.5, 0.4687), radius=.46,
clip_on=True, transform=plt.gca().transAxes)
# test 2: pass custom outlines with patch callable
outlines['patch'] = patch
plot_evoked_topomap(evoked, times, ch_type='eeg', outlines='head')
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
times, ch_type='eeg')
plt.close('all')
def draw(self, data, ch_label):
self.pos = np.array(list(channel_dict_2D.values()))
self.ch_names_ = list(channel_dict_2D.keys())
self.pos, self.outlines = topomap._check_outlines(self.pos, 'head')
center = 0.5 * (self.pos.max(axis=0) + self.pos.min(axis=0))
scale = 1.0 / (self.pos.max(axis=0) - self.pos.min(axis=0))
scale[0] = scale[0] * 1.3
self.plt_idx = [self.ch_names_.index(name) for name in ch_label]
ch_pos = np.array([self.pos[idx] * 5 / 6 for idx in self.plt_idx])
mask = np.ones((len(ch_pos), 1), dtype=bool)
for idx in range(8):
x_idx = idx // 4
y_idx = idx % 4
if len(self.plots) <= idx:
self.axes[x_idx][y_idx].set_title(self.titles[idx])
if self.parent.ch_loc:
marker = "."
else:
marker = ""
im, cont, interp = mne.viz.topomap._plot_topomap(
data[idx],
ch_pos,
axes=self.axes[x_idx][y_idx],
contours=10,
show=False,
mask=mask,
mask_params=dict(marker=marker),
head_pos=dict(center=center, scale=scale * 1.1),
cmap="RdBu_r",
names=ch_label,
show_names=self.parent.ch_loc)
self.colorbar_ax.set_position([0.9, 0.04, 0.03, 0.15])
self.plots.append([im, cont, interp])
else:
im, cont, interp = self.plots[idx]
Zi = interp.set_values(data[idx])()
im.set_data(Zi)
# must be removed and re-added
if len(cont.collections) > 0:
tp = cont.collections[0]
visible = tp.get_visible()
patch_ = tp.get_clip_path()
color = tp.get_color()
lw = tp.get_linewidth()
for tp in cont.collections[:]:
tp.remove()
cont = self.axes[x_idx][y_idx].contour(interp.Xi,
interp.Yi,
Zi,
10,
colors=color,
linewidths=lw)
for tp in cont.collections:
tp.set_visible(visible)
tp.set_clip_path(patch_)
self.plots[idx][1] = cont
self.plots[idx][2] = interp
self.fig.canvas.draw()
def test_plot_topomap():
"""Test topomap plotting."""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
res = 8
fast_test = dict(res=res, contours=0, sensors=False, time_unit='s')
fast_test_noscale = dict(res=res, contours=0, sensors=False)
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type='grad', times=[0, 0.1],
butterfly=False, time_unit='s')
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close('all')
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(TypeError, plt_topomap, head_pos='foo')
pytest.raises(KeyError, plt_topomap, head_pos=dict(foo='bar'))
pytest.raises(ValueError, plt_topomap, head_pos=dict(center=0))
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')
plt_topomap = partial(evoked.plot_topomap, **fast_test)
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]
assert (all('MEG' not in x.get_text()
for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# 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]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert ('head' in outlines.keys())
assert ('nose' in outlines.keys())
assert ('ear_left' in outlines.keys())
assert ('ear_right' in outlines.keys())
assert ('autoshrink' in outlines.keys())
assert (outlines['autoshrink'])
assert ('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert ('head' in outlines.keys())
assert ('nose' in outlines.keys())
assert ('ear_left' in outlines.keys())
assert ('ear_right' in outlines.keys())
assert ('autoshrink' in outlines.keys())
assert (not outlines['autoshrink'])
assert ('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos, 'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
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():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 16
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type='grad',
times=[0, 0.1],
butterfly=False)
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close('all')
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type='mag')
assert_raises(TypeError, ev_bad.plot_topomap, head_pos='foo')
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
evoked.plot_topomap(axes=axes, colorbar=False)
plt.close('all')
evoked.plot_topomap(times=[-0.1, 0.2])
plt.close('all')
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(ch_type='mag', outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type='eeg', res=res, scale=1)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res)
evoked.plot_topomap(times, ch_type='planar1', res=res)
evoked.plot_topomap(times, ch_type='planar2', res=res)
evoked.plot_topomap(times,
ch_type='grad',
mask=mask,
res=res,
show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
assert_raises(ValueError,
evoked.plot_topomap,
times,
ch_type='eeg',
res=res,
average=-1000)
assert_raises(ValueError,
evoked.plot_topomap,
times,
ch_type='eeg',
res=res,
average='hahahahah')
p = evoked.plot_topomap(times,
ch_type='grad',
res=res,
show_names=lambda x: x.replace('MEG', ''),
image_interp='bilinear')
subplot = [
x for x in p.get_children() if isinstance(x, matplotlib.axes.Subplot)
][0]
assert_true(
all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# 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)
# fail with multiple channel types
assert_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 = evoked.plot_topomap(times, ch_type='eeg', res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type='eeg', title='Custom', res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
evoked.plot_topomap(times, ch_type='mag', layout=None, res=res)
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
evoked.plot_topomap(0.1, 'mag', proj='interactive', res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter('always')
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs, res=res)
plt.close('all')
ax = plt.subplot(111)
plot_projs_topomap([projs[0]], res=res, axes=ax) # test axes param
plt.close('all')
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]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(not outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos,
'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt')
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines)
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)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_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))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_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)]
with warnings.catch_warnings(record=True): # rightmost column
evoked.plot_topomap(times='peaks', axes=axes)
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])
def test_plot_topomap():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 16
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0))
ev_bad = evoked.pick_types(meg=False, eeg=True, copy=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, should plot EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type='mag')
assert_raises(TypeError, ev_bad.plot_topomap, head_pos='foo')
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
evoked.plot_topomap(axes=axes, colorbar=False)
plt.close('all')
evoked.plot_topomap(times=[-0.1, 0.2])
plt.close('all')
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(ch_type='mag', outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type='eeg', res=res, scale=1)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res)
evoked.plot_topomap(times, ch_type='planar1', res=res)
evoked.plot_topomap(times, ch_type='planar2', res=res)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res,
show_names=True, mask_params={'marker': 'x'})
plt.close('all')
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average=-1000)
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average='hahahahah')
p = evoked.plot_topomap(times, ch_type='grad', res=res,
show_names=lambda x: x.replace('MEG', ''),
image_interp='bilinear')
subplot = [x for x in p.get_children() if
isinstance(x, matplotlib.axes.Subplot)][0]
assert_true(all('MEG' not in x.get_text()
for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if
isinstance(x, matplotlib.text.Text)]
p = evoked.plot_topomap(times, ch_type='eeg', res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type='eeg', title='Custom', res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
evoked.plot_topomap(times, ch_type='mag', layout=None, res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0), proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
evoked.plot_topomap(0.1, 'mag', proj='interactive', res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter('always')
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs, res=res)
plt.close('all')
ax = plt.subplot(111)
plot_projs_topomap([projs[0]], res=res, axes=ax) # test axes param
plt.close('all')
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]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(not outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos, 'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt')
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines)
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)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
times, ch_type='eeg')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_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))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_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)
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])
def plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True,
res=64, axes=None, names=None, show_names=False, mask=None,
mask_params=None, outlines='head', image_mask=None,
contours=6, image_interp='bilinear', show=True,
head_pos=None, onselect=None, axis=None):
''' see the docstring for mne.viz.plot_topomap,
which i've simply modified to return more objects '''
from matplotlib.widgets import RectangleSelector
from mne.io.pick import (channel_type, pick_info, _pick_data_channels)
from mne.utils import warn
from mne.viz.utils import (_setup_vmin_vmax, plt_show)
from mne.defaults import _handle_default
from mne.channels.layout import _find_topomap_coords
from mne.io.meas_info import Info
from mne.viz.topomap import _check_outlines, _prepare_topomap, _griddata, _make_image_mask, _plot_sensors, \
_draw_outlines
data = np.asarray(data)
if isinstance(pos, Info): # infer pos from Info object
picks = _pick_data_channels(pos) # pick only data channels
pos = pick_info(pos, picks)
# check if there is only 1 channel type, and n_chans matches the data
ch_type = set(channel_type(pos, idx)
for idx, _ in enumerate(pos["chs"]))
info_help = ("Pick Info with e.g. mne.pick_info and "
"mne.channels.channel_indices_by_type.")
if len(ch_type) > 1:
raise ValueError("Multiple channel types in Info structure. " +
info_help)
elif len(pos["chs"]) != data.shape[0]:
raise ValueError("Number of channels in the Info object and "
"the data array does not match. " + info_help)
else:
ch_type = ch_type.pop()
if any(type_ in ch_type for type_ in ('planar', 'grad')):
# deal with grad pairs
from ..channels.layout import (_merge_grad_data, find_layout,
_pair_grad_sensors)
picks, pos = _pair_grad_sensors(pos, find_layout(pos))
data = _merge_grad_data(data[picks]).reshape(-1)
else:
picks = list(range(data.shape[0]))
pos = _find_topomap_coords(pos, picks=picks)
if data.ndim > 1:
raise ValueError("Data needs to be array of shape (n_sensors,); got "
"shape %s." % str(data.shape))
# Give a helpful error message for common mistakes regarding the position
# matrix.
pos_help = ("Electrode positions should be specified as a 2D array with "
"shape (n_channels, 2). Each row in this matrix contains the "
"(x, y) position of an electrode.")
if pos.ndim != 2:
error = ("{ndim}D array supplied as electrode positions, where a 2D "
"array was expected").format(ndim=pos.ndim)
raise ValueError(error + " " + pos_help)
elif pos.shape[1] == 3:
error = ("The supplied electrode positions matrix contains 3 columns. "
"Are you trying to specify XYZ coordinates? Perhaps the "
"mne.channels.create_eeg_layout function is useful for you.")
raise ValueError(error + " " + pos_help)
# No error is raised in case of pos.shape[1] == 4. In this case, it is
# assumed the position matrix contains both (x, y) and (width, height)
# values, such as Layout.pos.
elif pos.shape[1] == 1 or pos.shape[1] > 4:
raise ValueError(pos_help)
if len(data) != len(pos):
raise ValueError("Data and pos need to be of same length. Got data of "
"length %s, pos of length %s" % (len(data), len(pos)))
norm = min(data) >= 0
vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm)
if cmap is None:
cmap = 'Reds' if norm else 'RdBu_r'
pos, outlines = _check_outlines(pos, outlines, head_pos)
if axis is not None:
axes = axis
warn('axis parameter is deprecated and will be removed in 0.13. '
'Use axes instead.', DeprecationWarning)
ax = axes if axes else plt.gca()
pos_x, pos_y = _prepare_topomap(pos, ax)
if outlines is None:
xmin, xmax = pos_x.min(), pos_x.max()
ymin, ymax = pos_y.min(), pos_y.max()
else:
xlim = np.inf, -np.inf,
ylim = np.inf, -np.inf,
mask_ = np.c_[outlines['mask_pos']]
xmin, xmax = (np.min(np.r_[xlim[0], mask_[:, 0]]),
np.max(np.r_[xlim[1], mask_[:, 0]]))
ymin, ymax = (np.min(np.r_[ylim[0], mask_[:, 1]]),
np.max(np.r_[ylim[1], mask_[:, 1]]))
# interpolate data
xi = np.linspace(xmin, xmax, res)
yi = np.linspace(ymin, ymax, res)
Xi, Yi = np.meshgrid(xi, yi)
Zi = _griddata(pos_x, pos_y, data, Xi, Yi)
if outlines is None:
_is_default_outlines = False
elif isinstance(outlines, dict):
_is_default_outlines = any(k.startswith('head') for k in outlines)
if _is_default_outlines and image_mask is None:
# prepare masking
image_mask, pos = _make_image_mask(outlines, pos, res)
mask_params = _handle_default('mask_params', mask_params)
# plot outline
linewidth = mask_params['markeredgewidth']
patch = None
if 'patch' in outlines:
patch = outlines['patch']
patch_ = patch() if callable(patch) else patch
patch_.set_clip_on(False)
ax.add_patch(patch_)
ax.set_transform(ax.transAxes)
ax.set_clip_path(patch_)
# plot map and countour
im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower',
aspect='equal', extent=(xmin, xmax, ymin, ymax),
interpolation=image_interp)
# This tackles an incomprehensible matplotlib bug if no contours are
# drawn. To avoid rescalings, we will always draw contours.
# But if no contours are desired we only draw one and make it invisible .
no_contours = False
if contours in (False, None):
contours, no_contours = 1, True
cont = ax.contour(Xi, Yi, Zi, contours, colors='k',
linewidths=linewidth)
if no_contours is True:
for col in cont.collections:
col.set_visible(False)
if _is_default_outlines:
from matplotlib import patches
patch_ = patches.Ellipse((0, 0),
2 * outlines['clip_radius'][0],
2 * outlines['clip_radius'][1],
clip_on=True,
transform=ax.transData)
if _is_default_outlines or patch is not None:
im.set_clip_path(patch_)
if cont is not None:
for col in cont.collections:
col.set_clip_path(patch_)
if sensors is not False and mask is None:
_plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax)
elif sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
idx = np.where(~mask)[0]
_plot_sensors(pos_x[idx], pos_y[idx], sensors=sensors, ax=ax)
elif not sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
if isinstance(outlines, dict):
_draw_outlines(ax, outlines)
if show_names:
if names is None:
raise ValueError("To show names, a list of names must be provided"
" (see `names` keyword).")
if show_names is True:
def _show_names(x):
return x
else:
_show_names = show_names
show_idx = np.arange(len(names)) if mask is None else np.where(mask)[0]
for ii, (p, ch_id) in enumerate(zip(pos, names)):
if ii not in show_idx:
continue
ch_id = _show_names(ch_id)
ax.text(p[0], p[1], ch_id, horizontalalignment='center',
verticalalignment='center', size='x-small')
plt.subplots_adjust(top=.95)
if onselect is not None:
ax.RS = RectangleSelector(ax, onselect=onselect)
plt_show(show)
return ax, im, cont, pos_x, pos_y
def test_plot_topomap():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 16
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0))
ev_bad = evoked.pick_types(meg=False, eeg=True, copy=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, should plot EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type='mag')
assert_raises(TypeError, ev_bad.plot_topomap, head_pos='foo')
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
evoked.plot_topomap(axes=axes, colorbar=False)
plt.close('all')
evoked.plot_topomap(times=[-0.1, 0.2])
plt.close('all')
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(None, ch_type='mag', outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type='eeg', res=res, scale=1)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res)
evoked.plot_topomap(times, ch_type='planar1', res=res)
evoked.plot_topomap(times, ch_type='planar2', res=res)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res,
show_names=True, mask_params={'marker': 'x'})
plt.close('all')
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average=-1000)
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average='hahahahah')
p = evoked.plot_topomap(times, ch_type='grad', res=res,
show_names=lambda x: x.replace('MEG', ''),
image_interp='bilinear')
subplot = [x for x in p.get_children() if
isinstance(x, matplotlib.axes.Subplot)][0]
assert_true(all('MEG' not in x.get_text()
for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if
isinstance(x, matplotlib.text.Text)]
p = evoked.plot_topomap(times, ch_type='eeg', res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type='eeg', title='Custom', res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
evoked.plot_topomap(times, ch_type='mag', layout=None, res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0), proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
evoked.plot_topomap(0.1, 'mag', proj='interactive', res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter('always')
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs, res=res)
plt.close('all')
ax = plt.subplot(111)
plot_projs_topomap([projs[0]], res=res, axes=ax) # test axes param
plt.close('all')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
if ch['eeg_loc'] is not None:
ch['eeg_loc'].fill(0)
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
pos = make_eeg_layout(evoked.info).pos
pos, outlines = _check_outlines(pos, 'head')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(not outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos, 'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt')
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines)
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)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
times, ch_type='eeg')
plt.close('all')