df = pd.DataFrame(np.concatenate(data), columns=channels)
df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])
df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])
return df, fs, p_names, channels
dir = r'C:\Users\Nikolai\Desktop\vibro-decay'
experiment = 'vibro-decay-vb-exp_01-26_17-42-57'
file = r'{}\{}\experiment_data.h5'.format(dir, experiment)
data, fs, p_names, channels = load_data(file)
for ch in []:
channels.remove(ch)
del data[ch]
data = data[~get_outliers_mask(data[channels], iter_numb=25, std=2.5)]
plt.plot(data[channels]*10000 + np.arange(len(channels)))
plt.show()
#data = data.iloc[~get_outliers_mask(data[channels], std=3, iter_numb=40)]
#data = data.drop(np.array(channels)[[0, 4, 9, 20]], axis=1)
#channels = [ch for j, ch in enumerate(channels) if j not in [0, 4, 9, 20]]
#data[channels] = fft_filter(data[channels], fs, (1, 45))
#data = data.iloc[~get_outliers_mask(data[channels], std=3, iter_numb=2)]
#plt.plot(data[channels] + 1000*np.arange(len(channels)))
#plt.show()
print('ww', sum(get_outliers_mask(data[channels][data['block_number']<3])))
# spatial filter (SMR detection)
try:
experiments = pd.read_csv(wdir + 'vibro-decay.csv')
experiments = experiments[experiments.protocol == 'belt']
print(experiments)
n_exp = 20
print(n_exp)
exp = experiments.iloc[n_exp]
desc = '{}-{}-{}-{}'.format(exp['subject'], exp['protocol'], {
0: 'exp',
1: 'control'
}[exp['control']], '-'.join(exp.dataset.split('_')[-2:]))
print(exp, '\n*******************', desc, '\n*******************')
df, fs, p_names, channels = load_data('{}{}/experiment_data.h5'.format(
data_dir, exp.dataset))
df = df[~get_outliers_mask(df[channels], std=3)]
right, left = runica2(
df.loc[df['block_number'].isin([1, 2, 3, 7, 8, 9]), channels], fs,
channels, ['RIGHT', 'LEFT'])
np.save(wdir + desc + '-RIGHT.npy', right)
np.save(wdir + desc + '-LEFT.npy', left)
# load and plot
right = np.load(wdir + desc + '-RIGHT.npy')
left = np.load(wdir + desc + '-LEFT.npy')
f, ax = plt.subplots(1, 4)
plot_topomap(right[0],
Montage(channels).get_pos(),
contours=0,
axes=ax[0],
df = pd.DataFrame(np.concatenate(data), columns=channels)
df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])
df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])
return df, fs, p_names, channels
dir = r'C:\Users\Nikolai\Desktop\vibro-decay'
experiment = 'vibro-decay-vb-exp_01-26_17-42-57'
file = r'{}\{}\experiment_data.h5'.format(dir, experiment)
data, fs, p_names, channels = load_data(file)
for ch in []:
channels.remove(ch)
del data[ch]
data = data[~get_outliers_mask(data[channels], iter_numb=25, std=2.5)]
plt.plot(data[channels]*10000 + np.arange(len(channels)))
plt.show()
#data = data.iloc[~get_outliers_mask(data[channels], std=3, iter_numb=40)]
#data = data.drop(np.array(channels)[[0, 4, 9, 20]], axis=1)
#channels = [ch for j, ch in enumerate(channels) if j not in [0, 4, 9, 20]]
#data[channels] = fft_filter(data[channels], fs, (1, 45))
#data = data.iloc[~get_outliers_mask(data[channels], std=3, iter_numb=2)]
#plt.plot(data[channels] + 1000*np.arange(len(channels)))
#plt.show()
print('ww', sum(get_outliers_mask(data[channels][data['block_number']<3])))
# spatial filter (SMR detection)
try:
data_dir = '/media/nikolai/D27ECFCB7ECFA697/Users/Nikolai/Desktop/vibro-decay/'
experiments = pd.read_csv(wdir + 'vibro-decay.csv')
experiments = experiments[experiments.protocol == 'belt']
print(experiments)
n_exp = 24
print(n_exp)
exp = experiments.iloc[n_exp]
desc = '{}-{}-{}-{}'.format(exp['subject'], exp['protocol'], {0: 'exp', 1:'control'}[exp['control']], '-'.join(exp.dataset.split('_')[-2:]))
print(exp, '\n*******************', desc, '\n*******************')
df, fs, p_names, channels = load_data('{}{}/experiment_data.h5'.format(data_dir, exp.dataset))
channels = channels[:32]
df = df[~get_outliers_mask(df[channels], std=3)]
right, left = runica2(df.loc[df['block_number'].isin([1, 2, 3, 7, 8, 9]), channels], fs, channels, ['RIGHT', 'LEFT'])
np.save(wdir + desc + '-RIGHT.npy', right)
np.save(wdir + desc + '-LEFT.npy', left)
# load and plot
right = np.load(wdir + desc + '-RIGHT.npy')
left = np.load(wdir + desc + '-LEFT.npy')
f, ax = plt.subplots(1, 4)
plot_topomap(right[0], Montage(channels).get_pos(), contours=0, axes=ax[0], show=False)
plot_topomap(right[1], Montage(channels).get_pos(), contours=0, axes=ax[1], show=False)
plot_topomap(left[0], Montage(channels).get_pos(), contours=0, axes=ax[2], show=False)
plot_topomap(left[1], Montage(channels).get_pos(), contours=0, axes=ax[3], show=False)
ax[0].set_title('Right spat.')
print(n_exp)
exp = experiments.iloc[n_exp]
if n_exp == 0:
continue
if exp['name'] in ['va-ba']:
print('\n'.join(['*********************************'] * 10))
continue
desc = '{}-{}-{}-{}'.format(exp['subject'], exp['protocol'], {
0: 'exp',
1: 'control'
}[exp['control']], '-'.join(exp.dataset.split('_')[-2:]))
print(exp, '\n*******************', desc, '\n*******************')
df, fs, p_names, channels = load_data('{}{}/experiment_data.h5'.format(
data_dir, exp.dataset))
df.iloc[get_outliers_mask(df[channels], std=3, iter_numb=20)] = 0
#channels = channels[:32]
channels = channels[:32]
right = np.load(wdir + desc + '-RIGHT.npy')[0]
left = np.load(wdir + desc + '-LEFT.npy')[0]
x = np.dot(df[channels], right)
f, t, Sxx = signal.spectrogram(x,
fs,
scaling='spectrum',
nperseg=fs * 10,
nfft=fs * 10,
noverlap=int(fs * 10 * 0.90))
channels = [
'Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2',
'Fc6', 'Ft10', 'T3', 'C3', 'Cz', 'C4', 'T4', 'Tp9', 'Cp5', 'Cp1', 'Cp2',
'Cp6', 'Tp10', 'T5', 'P3', 'Pz', 'P4', 'T6', 'O1', 'Oz', 'O2'
]
data = [
loadmat(
r'C:\Users\Nikolai\Desktop\Liza_diplom_data\Liza_diplom_data\treatment\p25\day2\proba{}.mat'
.format(k))['X1Topo'].T for k in range(1, 16)
]
df = pd.DataFrame(data=np.concatenate(data), columns=channels)
for ch in ['Pz', 'Cz']:
channels.remove(ch)
del df[ch]
df = df.loc[~get_outliers_mask(df[channels])]
#plt.plot(*welch(df['C3'], fs, nperseg=4*fs))
plt.plot(df[channels])
plt.show()
#plt.show()
#df['C3env'] = np.abs(hilbert(fft_filter(df['C3'], fs, band)))
#df['C4env'] = np.abs(hilbert(fft_filter(df['C4'], fs, band)))
#plt.plot(df['C3env']+df['C4env'])
a = QtGui.QApplication([])
(rej, filt, topo, _unmix, _bandpass,
_) = ICADialog.get_rejection(df.iloc[:fs * 60 * 3], channels, fs)
df['SMR'] = np.dot(df.as_matrix(), filt)
df.to_pickle('p4')
plt.plot(df['SMR'])
plt.show()
for n_exp in list(range(0 , len(experiments)))[::-1]:
print(n_exp)
exp = experiments.iloc[n_exp]
if n_exp == 0:
continue
if exp['name'] in ['va-ba']:
print('\n'.join(['*********************************']*10))
continue
desc = '{}-{}-{}-{}'.format(exp['subject'], exp['protocol'], {0: 'exp', 1:'control'}[exp['control']], '-'.join(exp.dataset.split('_')[-2:]))
print(exp, '\n*******************', desc, '\n*******************')
df, fs, p_names, channels = load_data('{}{}/experiment_data.h5'.format(data_dir, exp.dataset))
df.iloc[get_outliers_mask(df[channels], std=3, iter_numb=20)] = 0
#channels = channels[:32]
channels = channels[:32]
right = np.load(wdir + desc + '-RIGHT.npy')[0]
left = np.load(wdir + desc + '-LEFT.npy')[0]
x = np.dot(df[channels], right)
f, t, Sxx = signal.spectrogram(x, fs, scaling='spectrum', nperseg=fs*10, nfft=fs*10, noverlap=int(fs*10*0.90))
Sxx = Sxx**0.5
print(f.shape, t.shape, Sxx.shape)
norm = np.mean(Sxx[(f >= band[0]) & (f <= band[1])][:, t <= 120])
