def bootstrap_comparison(list_of_epochs,
stat_fun=None,
color_coding=None,
title=None,
subtitles=None,
vline=None,
ci=None):
"""
Compare different evoked responses
"""
if stat_fun is None:
stat_fun = gfp
cNorm = colors.Normalize(vmin=min(color_coding.values()),
vmax=max(color_coding.values()))
cmap = cmx.ScalarMappable(norm=cNorm, cmap="cool")
fig, ax = plt.subplots(len(list_of_epochs), sharex="all", sharey="all")
fig.colorbar(cmap, ax=ax)
if title is not None:
fig.suptitle("%s mode: %s" % (title))
for i, epochs in enumerate(list_of_epochs):
for event in epochs.event_id.keys():
data = epochs[event].get_data()
data_to_plot = stat_fun(data)
ax[i].plot(epochs.times,
data_to_plot,
color=cmap.to_rgba(color_coding[event]),
label=event)
if ci is not None:
ci_low, ci_high = bootstrap_confidence_interval(
data, ci=ci, stat_fun=stat_fun)
ax[i].fill_between(epochs.times,
ci_low,
ci_high,
color=cmap.to_rgba(color_coding[event]),
alpha=0.3)
if subtitles is not None:
ax[i].set_title(subtitles[i])
if vline is not None:
ax[i].axvline(vline, linestyle="--", c="black")
print(frequency_map)
del raw
# Plot
fig, axes = plt.subplots(4, 1, figsize=(10, 7), sharex=True, sharey=True)
colors = plt.get_cmap('winter_r')(np.linspace(0, 1, 4))
for ((freq_name, fmin, fmax),
average), color, ax in zip(frequency_map, colors,
axes.ravel()[::-1]):
times = average.times * 1e3
gfp = np.sum(average.data**2, axis=0)
gfp = mne.baseline.rescale(gfp, times, baseline=(None, 0))
ax.plot(times, gfp, label=freq_name, color=color, linewidth=2.5)
ax.axhline(0, linestyle='--', color='grey', linewidth=2)
ci_low, ci_up = bootstrap_confidence_interval(average.data,
random_state=0,
stat_fun=stat_fun)
ci_low = rescale(ci_low, average.times, baseline=(None, 0))
ci_up = rescale(ci_up, average.times, baseline=(None, 0))
ax.fill_between(times,
gfp + ci_up,
gfp - ci_low,
color=color,
alpha=0.3)
ax.grid(True)
ax.set_ylabel('GFP')
ax.annotate('%s (%d-%dHz)' % (freq_name, fmin, fmax),
xy=(0.95, 0.8),
horizontalalignment='right',
xycoords='axes fraction')
ax.set_xlim(tmin * 1000, tmax * 1000)
def compare_evokeds(dataset,
groups,
mode="gfp",
title=None,
subtitles=None,
vline=None,
color_coding=None,
ci=None):
"""
Compare different evoked responses
"""
cNorm = colors.Normalize(vmin=min(color_coding.values()),
vmax=max(color_coding.values()))
cmap = cmx.ScalarMappable(norm=cNorm, cmap="cool")
if mode == "gfp":
stat_fun = gfp
elif mode == "rms":
stat_fun = rms
else:
raise ValueError("Mode must be either 'rms' or 'gfp'!")
if isinstance(dataset, list) and all(
isinstance(d, Evoked) for d in dataset):
if ci is not None:
raise ValueError("Need Epochs to compute Confidence Interval")
names = [evoked.comment for evoked in dataset]
elif isinstance(dataset, Epochs) or isinstance(dataset, BaseEpochs):
names = list(dataset.event_id.keys())
else:
raise ValueError(
"Input must be either a list of evoked responses or epoched data")
fig, ax = plt.subplots(len(groups), sharex="all", sharey="all")
fig.colorbar(cmap, ax=ax)
if title is not None:
fig.suptitle("%s mode: %s" % (title, mode))
for i, group in enumerate(groups):
for g in group:
idx = numpy.where(numpy.array(names) == g)[0][0]
if isinstance(dataset, list): # data is evoked
data = dataset[idx]
else: # data is epochs
data = dataset[names[idx]]
times = data.times
if isinstance(dataset, list): # data is evoked
raw_data = data.data
else:
raw_data = data._data
data = stat_fun(raw_data)
ax[i].plot(times,
data,
color=cmap.to_rgba(color_coding[names[idx]]),
label=g)
if ci is not None:
ci_low, ci_high = bootstrap_confidence_interval(
raw_data, ci=ci, stat_fun=stat_fun)
ax[i].fill_between(times,
ci_low,
ci_high,
color=cmap.to_rgba(
color_coding[names[idx]]),
alpha=0.3)
if subtitles is not None:
ax[i].set_title(subtitles[i])
if vline is not None:
ax[i].axvline(vline, linestyle="--", c="black")
