def erds_plot(epochs):
# compute ERDS maps ###########################################################
# idx = np.array(list(range(7, 11)) + list(range(12, 15)) + list(range(17, 21)) + list(range(32, 41)))
# chans = np.array(epochs.ch_names)[idx].tolist()
epochs.pick_channels(["C3","Cz","C4"])
event_ids = epochs.event_id
tmin, tmax = -1, 4
freqs = np.arange(60, 90, 1) # frequencies from 2-35Hz
n_cycles = freqs # use constant t/f resolution
vmin, vmax = -1, 1.5 # set min and max ERDS values in plot
# vmin, vmax = -0.5, 0.5 # set min and max ERDS values in plot
baseline = [-1, 0.3] # baseline interval (in s)
cmap = center_cmap(plt.cm.RdBu, vmin, vmax) # zero maps to white
kwargs = dict(n_permutations=100, step_down_p=0.05, seed=1,
buffer_size=None, out_type='mask') # for cluster test
# Run TF decomposition overall epochs
tfr = tfr_multitaper(epochs, freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=False, average=False,
decim=2)
tfr.crop(tmin, tmax)
tfr.apply_baseline(baseline, mode="percent")
for event in event_ids:
# select desired epochs for visualization
tfr_ev = tfr[event]
fig, axes = plt.subplots(1, 4, figsize=(12, 4),
gridspec_kw={"width_ratios": [10, 10, 10, 1]})
for ch, ax in enumerate(axes[:-1]): # for each channel
# positive clusters
_, c1, p1, _ = pcluster_test(tfr_ev.data[:, ch, ...], tail=1, **kwargs)
# negative clusters
_, c2, p2, _ = pcluster_test(tfr_ev.data[:, ch, ...], tail=-1,
**kwargs)
# note that we keep clusters with p <= 0.05 from the combined clusters
# of two independent tests; in this example, we do not correct for
# these two comparisons
c = np.stack(c1 + c2, axis=2) # combined clusters
p = np.concatenate((p1, p2)) # combined p-values
mask = c[..., p <= 0.05].any(axis=-1)
# plot TFR (ERDS map with masking)
tfr_ev.average().plot([ch], vmin=vmin, vmax=vmax, cmap=(cmap, False),
axes=ax, colorbar=False, show=False, mask=mask,
mask_style="mask")
ax.set_title(epochs.ch_names[ch], fontsize=10)
ax.axvline(0, linewidth=1, color="black", linestyle=":") # event
if not ax.is_first_col():
ax.set_ylabel("")
ax.set_yticklabels("")
fig.colorbar(axes[0].images[-1], cax=axes[-1])
fig.suptitle("ERDS ({})".format(event))
fig.show()
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=False,
average=False,
decim=2)
tfr.crop(tmin, tmax)
tfr.apply_baseline(baseline, mode="percent")
fig, axes = plt.subplots(1,
4,
figsize=(12, 4),
gridspec_kw={"width_ratios": [10, 10, 10, 1]})
for ch, ax in enumerate(axes[:-1]): # for each channel
# positive clusters
_, c1, p1, _ = pcluster_test(tfr.data[:, ch, ...], tail=1, **kwargs)
# negative clusters
_, c2, p2, _ = pcluster_test(tfr.data[:, ch, ...], tail=-1, **kwargs)
# note that we keep clusters with p <= 0.05 from the combined clusters
# of two independent tests; in this example, we do not correct for
# these two comparisons
c = np.stack(c1 + c2, axis=2) # combined clusters
p = np.concatenate((p1, p2)) # combined p-values
mask = c[..., p <= 0.05].any(axis=-1)
# plot TFR (ERDS map)
tfr.average().plot([ch],
vmin=vmin,
vmax=vmax,
cmap=(cmap, False),
buffer_size=None) # for cluster test
# Run TF decomposition overall epochs
tfr = tfr_multitaper(epochs, freqs=freqs, n_cycles=n_cycles,
use_fft=True, return_itc=False, average=False,
decim=2)
tfr.crop(tmin, tmax)
tfr.apply_baseline(baseline, mode="percent")
for event in event_ids:
# select desired epochs for visualization
tfr_ev = tfr[event]
fig, axes = plt.subplots(1, 4, figsize=(12, 4),
gridspec_kw={"width_ratios": [10, 10, 10, 1]})
for ch, ax in enumerate(axes[:-1]): # for each channel
# positive clusters
_, c1, p1, _ = pcluster_test(tfr_ev.data[:, ch, ...], tail=1, **kwargs)
# negative clusters
_, c2, p2, _ = pcluster_test(tfr_ev.data[:, ch, ...], tail=-1,
**kwargs)
# note that we keep clusters with p <= 0.05 from the combined clusters
# of two independent tests; in this example, we do not correct for
# these two comparisons
c = np.stack(c1 + c2, axis=2) # combined clusters
p = np.concatenate((p1, p2)) # combined p-values
mask = c[..., p <= 0.05].any(axis=-1)
# plot TFR (ERDS map with masking)
tfr_ev.average().plot([ch], vmin=vmin, vmax=vmax, cmap=(cmap, False),
axes=ax, colorbar=False, show=False, mask=mask,
mask_style="mask")