def test_auto_topomap_coords():
"""Test mapping of coordinates in 3D space to 2D"""
info = Raw(fif_fname).info.copy()
picks = pick_types(info, meg=False, eeg=True, eog=False, stim=False)
# Remove extra digitization point, so EEG digitization points match up
# with the EEG channels
del info['dig'][85]
# Remove head origin from channel locations, so mapping with digitization
# points yields the same result
dig_kinds = (FIFF.FIFFV_POINT_CARDINAL,
FIFF.FIFFV_POINT_EEG,
FIFF.FIFFV_POINT_EXTRA)
_, origin_head, _ = fit_sphere_to_headshape(info, dig_kinds)
origin_head /= 1000. # to meters
for ch in info['chs']:
ch['loc'][:3] -= origin_head
# Use channel locations
l0 = _auto_topomap_coords(info, picks)
# Remove electrode position information, use digitization points from now
# on.
for ch in info['chs']:
ch['loc'] = np.zeros(12)
l1 = _auto_topomap_coords(info, picks)
assert_allclose(l1, l0)
# Test plotting mag topomap without channel locations: it should fail
mag_picks = pick_types(info, meg='mag')
assert_raises(ValueError, _auto_topomap_coords, info, mag_picks)
# Test function with too many EEG digitization points: it should fail
info['dig'].append({'r': [1, 2, 3], 'kind': FIFF.FIFFV_POINT_EEG})
assert_raises(ValueError, _auto_topomap_coords, info, picks)
# Test function with too little EEG digitization points: it should fail
info['dig'] = info['dig'][:-2]
assert_raises(ValueError, _auto_topomap_coords, info, picks)
# Electrode positions must be unique
info['dig'].append(info['dig'][-1])
assert_raises(ValueError, _auto_topomap_coords, info, picks)
# Test function without EEG digitization points: it should fail
info['dig'] = [d for d in info['dig'] if d['kind'] != FIFF.FIFFV_POINT_EEG]
assert_raises(RuntimeError, _auto_topomap_coords, info, picks)
# Test function without any digitization points, it should fail
info['dig'] = None
assert_raises(RuntimeError, _auto_topomap_coords, info, picks)
info['dig'] = []
assert_raises(RuntimeError, _auto_topomap_coords, info, picks)
def plot_montage(chan_list, ax=None, scores=None, title='', cmap=None):
"""Plot montage and color code channels."""
from mne.viz.utils import _plot_sensors
from mne.channels.layout import _auto_topomap_coords
from mne.channels import read_montage
from mne.utils import check_version
from matplotlib import cm
if cmap is None:
cmap = cm.viridis
# connectivity, ch_names = read_ch_connectivity(
# 'biosemi64', picks=montage.selection[:64])
# plt.imshow(connectivity.toarray(), origin='lower')
# plt.xlabel('{} Channels'.format(len(ch_names)))
# plt.ylabel('{} Channels'.format(len(ch_names)))
# plt.title('Between-sensor adjacency')
# plt.show()
montage = read_montage('biosemi64')
info = mne.create_info(montage.ch_names[:64], sfreq=256, ch_types="eeg",
montage='biosemi64')
pos = _auto_topomap_coords(info, picks=range(64), ignore_overlap=True,
to_sphere=True)
colors = np.ones((64, 4))
colors[:, 0:3] = 0.9
if not chan_list or chan_list is None:
chan_list = montage.ch_names
picks = mne.pick_channels(montage.ch_names[:64], chan_list)
if scores is None:
colors[picks, :] = cmap(100)
else:
for ch in chan_list:
picks2 = mne.pick_channels(montage.ch_names[:64], [ch, ])
colors[picks2, :] = cmap(scores[ch])
f = _plot_sensors(pos, colors, [], montage.ch_names[:64], title=title,
show_names=False,
ax=ax,
show=False,
select=False,
block=False,
to_sphere=True)
# Modify dot size
scale_factor = 20.
try:
ax = ax
except: # noqa
ax = f.axes[0]
collection = ax.collections[0]
if check_version("matplotlib", "1.4"):
collection.set_sizes([scale_factor])
collection.set_linewidths([0])
else:
collection._sizes = [scale_factor]
collection._linewidths = [0]
return f
