ax[1].cla()
ax[0].imshow(a, cmap=colour_map)
mne.viz.plot_topomap(np.random.random(32),
ch2[:, [1, 0]],
names=ch_names32,
show_names=True,
axes=ax[1],
cmap=colour_map,
show=False,
contours=False)
fig.canvas.draw()
colour_map = "viridis"
channels_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/static/chanlocs_mod.mat"
ch3 = parse_channels_locations_from_mat(channels_path)
ch2 = np.delete(ch3, 2, 1)
a = np.identity(15) * 1.5 + np.random.random([15, 15])
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))
ax[0].imshow(a, cmap=colour_map)
mne.viz.plot_topomap(np.arange(32),
ch2[:, [1, 0]],
names=ch_names32,
show_names=True,
axes=ax[1],
cmap=colour_map,
show=False,
contours=False)
channels_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/static/chanlocs_mod.mat"
considered_protocols = ['Closed', 'Baseline']
real_list = []
mock_list = []
# for i in range(len(sub)):
for i in range(1):
df, fs, channels = load_data(load_path.format(sub[i]))
try:
channels.remove("T7")
except ValueError:
pass
ch3, channels_in_list, ind_in_list = parse_channels_locations_from_mat(
channels_path, channels)
ch2 = np.delete(ch3, 2, 1)
df1 = df[df['block_name'] ==
considered_protocols[0]][channels_in_list].T.as_matrix()
df2 = df[df['block_name'] ==
considered_protocols[1]][channels_in_list].T.as_matrix()
#
window = signal.get_window(window="hamming", Nx=fs)
F, Pxx_1 = signal.welch(x=df1,
window=window,
noverlap=0.5 * fs,
nfft=5 * fs,
fs=fs)
# F, Pxx_2 = signal.welch(x=df2, window=window, noverlap=0.5*fs, nfft=5*fs, fs=fs)
# F, Pxx_1 = signal.welch(x=df1, fs=fs)
events = np.where(np.diff((x_m > y_m) * 1) > 0)[0]
trials_m = []
c = 0
for ev in events:
if ev - c > 3 * fs:
trials_m.append(np.arange(ev, ev + 1000))
c = ev
trials_r = np.array(trials_r)
trials_m = np.array(trials_m)
# Load Channels Positions
channels_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/static/chanlocs_mod.mat"
ch3, channels_in_list, ind_in_list = parse_channels_locations_from_mat(
channels_path, df.columns.values)
ch2 = np.delete(ch3, 2, 1)
# Run test
p_value, p_value_rightsided, p_value_leftsided, vectors_list, values_list, frequencies = test_rolling_variance_maximisation(
df, trials_r, trials_m, channels_in_list)
# Save data
names = [
'p_value', 'p_value_rightsided', 'p_value_leftsided', 'vectors_list',
'values_list', 'frequencies'
]
vars = [
p_value, p_value_rightsided, p_value_leftsided, vectors_list,