def plot_power_time(tfrs,
picks,
frequency,
pick_names=[],
event_names=[],
ncol=3):
# find the frequency index
nplots = len(picks) + 1 # last will be legend
nrow, ncol = helpers.nrow_ncol(nplots, ncol)
gs = gridspec.GridSpec(nrow, ncol)
fig = plt.figure()
for i, pick in enumerate(picks):
ax = fig.add_subplot(gs[i])
for n, event_tfr in enumerate(tfrs):
ax.plot(event_tfr.times,
event_tfr.data[pick, frequency, :],
label=event_names[n])
ax.legend([create_pick_name(i, pick_names)])
# adds legend
ax = fig.add_subplot(gs[nplots - 1])
for n, _ in enumerate(tfrs):
ax.plot([0], label=event_names[n])
handles, labels = ax.get_legend_handles_labels()
plt.legend(handles, labels)
plt.show()
def custom_box_layout(ch_names, ncol=6):
# Creates boundaries of the 0-1 box, will be
box = (-0.1, 1.1, -0.1, 1.1)
# just puts the channel names to strings
nrow, ncol = helpers.nrow_ncol(len(ch_names), ncol=ncol)
x, y = np.mgrid[0:1:(1 / ncol), 0:1:(1 / nrow)]
xy = np.vstack([x.ravel(), y.ravel()]).T
w, h = [1 / (1.1 * ncol), 1 / (1.1 * nrow)]
w, h = [np.tile(i, xy.shape[0]) for i in [w, h]]
pos = np.hstack([xy, w[:, None], h[:, None]])
# removes redundat column
pos = pos[:len(ch_names)]
ch_indices = range(len(ch_names))
box_layout = Layout(box, pos, ch_names, ch_indices, 'box')
return (box_layout)
def plot_power_time_average(tfrs,
picks,
frequency,
channel_name=[],
event_names=[],
ncol=3):
# find the frequency index
nplots = len(picks) + 1 # last will be legend
nrow, ncol = helpers.nrow_ncol(nplots, ncol)
plt.figure()
ax = plt.axes()
for n, event_tfr in enumerate(tfrs):
data = event_tfr.data[picks, frequency, :]
data = np.average(data, 0).squeeze()
ax.plot(event_tfr.times, data, label=event_names[n])
ax.legend(channel_name)
plt.legend()
plt.show()
def plot_epochs_power(epochs, frequency, picks=None):
epochsTFR = epochs.copy()
if not isinstance(epochsTFR, tfr.EpochsTFR):
print('Not Epochs TFR object')
return
if picks is not None:
epochsTFR.pick_channels(picks)
# recalculates to seconds from sampling freq
n_epochs = epochsTFR.data.shape[0]
x, y = np.meshgrid(epochsTFR.times, range(n_epochs))
nrow, ncol = helpers.nrow_ncol(epochsTFR.data.shape[1])
gs = gridspec.GridSpec(nrow, ncol)
fig = plt.figure()
for i, channel in enumerate(epochsTFR.ch_names):
ax = fig.add_subplot(gs[i])
# recalculates to seconds from sampling freq
values = np.array(epochsTFR.data[:, i, frequency, :])
im = ax.pcolormesh(x, y, values)
ax.set_title(epochsTFR.ch_names[i])
fig.subplots_adjust(right=0.8)
fig.colorbar(im)
plt.show()
def plot_wilcox_box(wilcox_table,
sampling_frequency,
cutout=0.05,
freqs=[],
picks=[],
pick_names=[]):
if len(freqs) == 0:
freqs = range(len(wilcox_table[0]) + 1)
if len(picks) == 0:
picks = range(len(wilcox_table))
# find the frequency index
times = range(len(wilcox_table[0][0]))
times = [time / sampling_frequency for time in times]
x, y = np.meshgrid(times, freqs)
nrow, ncol = helpers.nrow_ncol(len(picks))
gs = gridspec.GridSpec(nrow, ncol)
fig = plt.figure()
# pick the desired colormap, sensible levels, and define a normalization
# instance which takes data values and translates those into levels.
cmap, norm = mneplothelp.significance_plot_norms(cutout)
for i, channel in enumerate(picks):
ax = fig.add_subplot(gs[i])
#recalculates to seconds from sampling freq
p_values = np.array(wilcox_table[i])
p_values[p_values > cutout] = 1
p_values[p_values < -cutout] = -1
p_values[(p_values > 0) & (
p_values <= cutout)] = cutout - 0.0001 #because of how >< works
p_values[(p_values < 0) & (p_values >= -cutout)] = -cutout + 0.0001
im = ax.pcolormesh(x, y, p_values, cmap=cmap, norm=norm)
ax.set_title([mnehelp.create_pick_name(i, pick_names)])
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
plt.show()