def plot_topo_and_psd(inst, mean_psd, freqs, channels):
from matplotlib import gridspec
import matplotlib.pyplot as plt
from mne.viz.topomap import plot_psds_topomap
fig = plt.figure(figsize=(8, 3))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 2])
ax = [plt.subplot(g) for g in gs]
plot_psds_topomap(psds=mean_psd,
freqs=freqs,
pos=inst.info,
dB=False,
axes=[ax[0]],
bands=[(4., 8., 'Theta')],
normalize=False,
cmap='inferno',
show=False)
# highlight channels
circles = ax[0].findobj(plt.Circle)
for ch in channels:
circles[ch].set_color('r')
circles[ch].set_radius(0.025)
plot_freq = (freqs > 1.) & (freqs < 15.)
ax[1].plot(freqs[plot_freq], mean_psd[channels, :][:, plot_freq].T)
chan_avg = mean_psd[channels, :].mean(axis=0)
ax[1].plot(freqs[plot_freq], chan_avg[plot_freq], color='k', lw=2)
def test_plot_tfr_topomap():
"""Test plotting of TFR data."""
import matplotlib as mpl
import matplotlib.pyplot as plt
raw = read_raw_fif(raw_fname)
times = np.linspace(-0.1, 0.1, 200)
res = 8
n_freqs = 3
nave = 1
rng = np.random.RandomState(42)
picks = [93, 94, 96, 97, 21, 22, 24, 25, 129, 130, 315, 316, 2, 5, 8, 11]
info = pick_info(raw.info, picks)
data = rng.randn(len(picks), n_freqs, len(times))
tfr = AverageTFR(info, data, times, np.arange(n_freqs), nave)
tfr.plot_topomap(ch_type='mag',
tmin=0.05,
tmax=0.150,
fmin=0,
fmax=10,
res=res,
contours=0)
eclick = mpl.backend_bases.MouseEvent('button_press_event',
plt.gcf().canvas, 0, 0, 1)
eclick.xdata = eclick.ydata = 0.1
eclick.inaxes = plt.gca()
erelease = mpl.backend_bases.MouseEvent('button_release_event',
plt.gcf().canvas, 0.9, 0.9, 1)
erelease.xdata = 0.3
erelease.ydata = 0.2
pos = [[0.11, 0.11], [0.25, 0.5], [0.0, 0.2], [0.2, 0.39]]
_onselect(eclick, erelease, tfr, pos, 'grad', 1, 3, 1, 3, 'RdBu_r', list())
_onselect(eclick, erelease, tfr, pos, 'mag', 1, 3, 1, 3, 'RdBu_r', list())
eclick.xdata = eclick.ydata = 0.
erelease.xdata = erelease.ydata = 0.9
tfr._onselect(eclick, erelease, None, 'mean', None)
plt.close('all')
# test plot_psds_topomap
info = raw.info.copy()
chan_inds = channel_indices_by_type(info)
info = pick_info(info, chan_inds['grad'][:4])
fig, axes = plt.subplots()
freqs = np.arange(3., 9.5)
bands = [(4, 8, 'Theta')]
psd = np.random.rand(len(info['ch_names']), freqs.shape[0])
plot_psds_topomap(psd, freqs, info, bands=bands, axes=[axes])
def test_plot_tfr_topomap():
"""Test plotting of TFR data."""
import matplotlib as mpl
import matplotlib.pyplot as plt
raw = read_raw_fif(raw_fname)
times = np.linspace(-0.1, 0.1, 200)
res = 8
n_freqs = 3
nave = 1
rng = np.random.RandomState(42)
picks = [93, 94, 96, 97, 21, 22, 24, 25, 129, 130, 315, 316, 2, 5, 8, 11]
info = pick_info(raw.info, picks)
data = rng.randn(len(picks), n_freqs, len(times))
tfr = AverageTFR(info, data, times, np.arange(n_freqs), nave)
tfr.plot_topomap(ch_type='mag', tmin=0.05, tmax=0.150, fmin=0, fmax=10,
res=res, contours=0)
eclick = mpl.backend_bases.MouseEvent('button_press_event',
plt.gcf().canvas, 0, 0, 1)
eclick.xdata = eclick.ydata = 0.1
eclick.inaxes = plt.gca()
erelease = mpl.backend_bases.MouseEvent('button_release_event',
plt.gcf().canvas, 0.9, 0.9, 1)
erelease.xdata = 0.3
erelease.ydata = 0.2
pos = [[0.11, 0.11], [0.25, 0.5], [0.0, 0.2], [0.2, 0.39]]
_onselect(eclick, erelease, tfr, pos, 'grad', 1, 3, 1, 3, 'RdBu_r', list())
_onselect(eclick, erelease, tfr, pos, 'mag', 1, 3, 1, 3, 'RdBu_r', list())
eclick.xdata = eclick.ydata = 0.
erelease.xdata = erelease.ydata = 0.9
tfr._onselect(eclick, erelease, None, 'mean', None)
plt.close('all')
# test plot_psds_topomap
info = raw.info.copy()
chan_inds = channel_indices_by_type(info)
info = pick_info(info, chan_inds['grad'][:4])
fig, axes = plt.subplots()
freqs = np.arange(3., 9.5)
bands = [(4, 8, 'Theta')]
psd = np.random.rand(len(info['ch_names']), freqs.shape[0])
plot_psds_topomap(psd, freqs, info, bands=bands, axes=[axes])
def plot_topo_and_psd(inst, mean_psd, freqs, channels):
from matplotlib import gridspec
import matplotlib.pyplot as plt
from mne.viz.topomap import plot_psds_topomap
fig = plt.figure(figsize=(8, 3))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 2])
ax = [plt.subplot(g) for g in gs]
plot_psds_topomap(psds=mean_psd, freqs=freqs, pos=inst.info,
dB=False, axes=[ax[0]], bands=[(4., 8., 'Theta')],
normalize=False, cmap='inferno', show=False);
# highlight channels
circles = ax[0].findobj(plt.Circle)
for ch in channels:
circles[ch].set_color('r')
circles[ch].set_radius(0.025)
plot_freq = (freqs > 1.) & (freqs < 15.)
ax[1].plot(freqs[plot_freq], mean_psd[channels, :][:, plot_freq].T);
chan_avg = mean_psd[channels, :].mean(axis=0)
ax[1].plot(freqs[plot_freq], chan_avg[plot_freq], color='k', lw=2)
) = _prepare_topomap_plot(ep_low, ch_type, sphere=None)
outlines = _make_head_outlines(sphere, pos, "head", clip_origin)
if merge_channels:
psds_merge, names = _merge_ch_data(psds, ch_type, names, method="mean")
else:
psds_merge = psds
fig = plot_psds_topomap(
psds_merge,
freqs,
dB=False,
# vlim=(1, 1.5),
bands=[
(2, 4, "delta (2-4)"),
(4, 8, "theta (4-8)"),
(8, 12, "alpha (8-12)"),
(12, 30, "beta (12-30)"),
],
pos=pos,
outlines=outlines,
sphere=sphere,
show=False,
)
fig.set_size_inches((20, 10))
plt.show()
adjacency, ch_names = find_ch_adjacency(info, ch_type=ch_type)
# set cluster threshold
threshold = 6.0 # very high, but the test is quite sensitive on this data
# set family-wise p-value
p_accept = 0.05
