def get_some_data(real=False):
if not real:
channels = [
'Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5',
'Fc1', 'Fc2', 'Fc6', 'Ft10', 'T7', 'C3', 'Cz', 'C4', 'T8',
'Tp9', 'Cp5', 'Cp1', 'Cp2', 'Cp6', 'Tp10', 'P7', 'P3', 'Pz',
'P4', 'P8', 'O1', 'Oz', 'O2'
]
data = np.random.normal(loc=0,
scale=0.00001,
size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(
r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5'
) as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(
p_names.index('Baseline') + 1)][:].T
from PyQt4.QtGui import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T,
channels,
fs,
mode='ica',
states=None)[:3]
data = rej.apply(data.T).T
return data, fs, channels
def load_data(file_path):
with h5py.File(file_path) as f:
fs, channels, p_names = get_info(f, ['A1', 'A2'])
data = [f['protocol{}/raw_data'.format(k + 1)][:] for k in range(len(p_names))]
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
def load_data(file_path):
with h5py.File(file_path) as f:
fs, channels, p_names = get_info(f, ['A1', 'A2'])
data = [f['protocol{}/raw_data'.format(k + 1)][:] for k in range(len(p_names))]
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
def get_some_data(real=False):
if not real:
channels = ['Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2', 'Fc6', 'Ft10', 'T7', 'C3', 'Cz',
'C4', 'T8', 'Tp9', 'Cp5', 'Cp1', 'Cp2', 'Cp6', 'Tp10', 'P7', 'P3', 'Pz', 'P4', 'P8', 'O1', 'Oz', 'O2']
data = np.random.normal(loc=0, scale=0.00001, size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5') as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(p_names.index('Baseline') + 1)][:].T
from PyQt5.QtWidgets import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T, channels, fs, mode='ica', states=None)[:3]
data = rej.apply(data.T).T
return data, fs, channels
dir_ = settings['dir']
subj = 3
experiments = settings['subjects'][subj]
experiment = experiments[2]
def preproc(x, fs, rej=None):
x = dc_blocker(x)
x = fft_filter(x, fs, band=(0, 45))
if rej is not None:
x = np.dot(x, rej)
return x
reject = False
with h5py.File('{}\\{}\\{}'.format(settings['dir'], experiment, 'experiment_data.h5')) as f:
fs, channels, p_names = get_info(f, settings['drop_channels'])
rejection, alpha, ica = load_rejections(f, reject_alpha=True)
raw_before = OrderedDict()
raw_after = OrderedDict()
for j, name in enumerate(p_names):
if j < 5:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
raw_before = add_data_simple(raw_before, name, x)
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
# plot raw data
real = False
if not real:
channels = [
'Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2',
'Fc6', 'Ft10', 'T7', 'C3', 'Cz', 'C4', 'T8', 'Tp9', 'Cp5', 'Cp1',
'Cp2', 'Cp6', 'Tp10', 'P7', 'P3', 'Pz', 'P4', 'P8', 'O1', 'Oz', 'O2'
]
data = np.random.normal(loc=0, scale=0.00001, size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(
r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5'
) as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(p_names.index('Baseline') +
1)][:].T
from PyQt4.QtGui import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T,
channels,
fs,
mode='ica',
states=None)[:3]
data = rej.apply(data.T).T
# create info
info = mne.create_info(ch_names=channels,
sfreq=fs,
montage=mne.channels.read_montage(kind='standard_1005'),
# set trans
trans = r'C:\Users\nsmetanin\PycharmProjects\nfb\tests\sloreta\av_brain\fsaverage\bem\fsaverage-trans.fif'
# data
real = False
if not real:
channels = ['Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'Ft9', 'Fc5', 'Fc1', 'Fc2', 'Fc6', 'Ft10', 'T7', 'C3', 'Cz',
'C4', 'T8', 'Tp9', 'Cp5', 'Cp1', 'Cp2', 'Cp6', 'Tp10', 'P7', 'P3', 'Pz', 'P4', 'P8', 'O1', 'Oz', 'O2']
data = np.random.normal(loc=0, scale=0.00001, size=(5000, len(channels))).T
fs = 500
else:
import h5py
from pynfb.postprocessing.utils import get_info
with h5py.File(r'D:\mu_ica\mu_ica\mu_ica_S1_D3_04-21_18-16-03\experiment_data.h5') as f:
fs, channels, p_names = get_info(f, [])
data = f['protocol{}/raw_data'.format(p_names.index('Baseline') + 1)][:].T
from PyQt5.QtWidgets import QApplication
a = QApplication([])
rej, spatial, top = ICADialog.get_rejection(data.T, channels, fs, mode='ica', states=None)[:3]
data = rej.apply(data.T).T
standard_montage = mne.channels.read_montage(kind='standard_1005')
standard_montage_names = [name.upper() for name in standard_montage.ch_names]
for j, channel in enumerate(channels):
channels[j] = standard_montage.ch_names[standard_montage_names.index(channel.upper())]
# create info
info = mne.create_info(ch_names=channels, sfreq=fs, montage=mne.channels.read_montage(kind='standard_1005'), ch_types=['eeg' for ch in channels])
# raw instance
raw = mne.io.RawArray(data, info)
eeg_data = eeg_data[
without_emp_mask, :].T # Remove constant (zero) channels and prespecified channels
ch_names = [
name[0][0] for name in chan_names[:, without_emp_mask].T
]
labels = states_labels1[0]
datasets.append((eeg_data, labels))
else:
import h5py
import pylab as plt
file = r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\mu_ica_000000_04-17_19-11-50\experiment_data.h5'
labels_map = {'Open': 6, 'Right': 2, 'Left': 1}
with h5py.File(file) as f:
fs, ch_names, p_names = get_info(f, [])
before = []
after = []
before_labels = []
after_labels = []
k = 0
for protocol in [
'protocol{}'.format(j + 1)
for j in range(len(f.keys()) - 3)
]:
name = f[protocol].attrs['name']
if name in ['Right', 'Open', 'Left']:
data = f[protocol + '/raw_data'][:]
labels = np.ones(len(data), dtype=int) * labels_map[name]
if k < 3:
before.append(data)
fs = fs[0, 0]
nozeros_mask = np.sum(eeg_data[:, :fs * 2], 1) != 0 # Detect constant (zero) channels
without_emp_mask = nozeros_mask & (chan_names[0, :] != 'A1') & (chan_names[0, :] != 'A2') & (chan_names[0, :] != 'AUX')
eeg_data = eeg_data[without_emp_mask, :].T # Remove constant (zero) channels and prespecified channels
ch_names = [name[0][0] for name in chan_names[:, without_emp_mask].T]
labels = states_labels1[0]
datasets.append((eeg_data, labels))
else:
import h5py
import pylab as plt
file = r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\mu_ica_000000_04-17_19-11-50\experiment_data.h5'
labels_map = {'Open': 6, 'Right': 2, 'Left': 1}
with h5py.File(file) as f:
fs, ch_names, p_names = get_info(f, [])
before = []
after = []
before_labels = []
after_labels = []
k = 0
for protocol in ['protocol{}'.format(j + 1) for j in range(len(f.keys()) - 3)]:
name = f[protocol].attrs['name']
if name in ['Right', 'Open', 'Left']:
data = f[protocol + '/raw_data'][:]
labels = np.ones(len(data), dtype=int) * labels_map[name]
if k < 3:
before.append(data)
before_labels.append(labels)
else:
after.append(data)
if __name__ == '__main__':
np.random.seed(42)
# loading anp pre processing
import h5py
import pylab as plt
file = r'E:\Develop\NFB\pynfb\results\experiment_02-06_12-36-50\experiment_data.h5'
#file = r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\BCI_Test_2_1_06-22_17-01-07\experiment_data.h5'
labels_map = {'Open': 0, 'Right': 2, 'Left': 1}
with h5py.File(file) as f:
fs, ch_names, p_names = get_info(f,
[]) #['AUX', 'A1', 'A2', 'F4', 'Pz']
print(f.keys())
before = []
after = []
before_labels = []
after_labels = []
k = 0
for protocol in [
'protocol0'
]: #['protocol{}'.format(j + 1) for j in range(len(f.keys()) - 3)]:
name = f[protocol].attrs['name']
print(str(f[protocol].attrs['name']))
if name in ['unknown', 'Right', 'Open', 'Left']:
data = f[protocol + '/raw_data'][:]
labels = np.ones(len(data), dtype=int) * labels_map[name]
if k < 9:
def plot_results(pilot_dir, subj, channel, alpha_band=(9, 14), theta_band=(3, 6), drop_channels=None, dc=False,
reject_alpha=True, normalize_by='opened'):
drop_channels = drop_channels or []
cm = get_colors()
fg = plt.figure(figsize=(30, 6))
for j_s, experiment in enumerate(subj):
with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment, 'experiment_data.h5')) as f:
rejections, top_alpha, top_ica = load_rejections(f, reject_alpha=reject_alpha)
fs, channels, p_names = get_info(f, drop_channels)
ch = channels.index(channel)
#plt.plot(fft_filter(f['protocol6/raw_data'][:, ch], fs, band=(3, 35)))
#plt.plot(fft_filter(np.dot(f['protocol6/raw_data'], rejections)[:, ch], fs, band=(3, 35)))
#plt.show()
#from scipy.signal import welch
#plt.plot(*welch(f['protocol1/raw_data'][:60*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol1/raw_data'][60*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][:30*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][30*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.legend(['Close', 'Open', 'Left', 'Right'])
#plt.show()
# collect powers
powers = OrderedDict()
raw = OrderedDict()
alpha = OrderedDict()
pow_theta = []
for j, name in enumerate(p_names):
pow, alpha_x, x = get_protocol_power(f, j, fs, rejections, ch, alpha_band, dc=dc)
if 'FB' in name:
pow_theta.append(get_protocol_power(f, j, fs, rejections, ch, theta_band, dc=dc)[0].mean())
powers = add_data(powers, name, pow, j)
raw = add_data(raw, name, x, j)
alpha = add_data(alpha, name, alpha_x, j)
# plot rejections
n_tops = top_ica.shape[1] + top_alpha.shape[1]
for j_t in range(top_ica.shape[1]):
ax = fg.add_subplot(4, n_tops * len(subj), n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1)
ax.set_xlabel('ICA{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [label for label in labels if label not in drop_channels]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_ica[:, j_t], pos=pos, axes=ax, show=False)
for j_t in range(top_alpha.shape[1]):
ax = fg.add_subplot(4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1 + top_ica.shape[1])
ax.set_xlabel('CSP{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [label for label in labels if label not in drop_channels]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_alpha[:, j_t], pos=pos, axes=ax, show=False)
# plot powers
if normalize_by == 'opened':
norm = powers['1. Opened'].mean()
elif normalize_by == 'beta':
norm = np.mean(pow_theta)
else:
print('WARNING: norm = 1')
print('norm', norm)
ax1 = fg.add_subplot(3, len(subj), j_s + 1)
ax = fg.add_subplot(3, len(subj), j_s + len(subj) + 1)
t = 0
for j_p, ((name, pow), (name, x)) in enumerate(zip(powers.items(), raw.items())):
if name == '2228. FB':
from scipy.signal import periodogram
fff = plt.figure()
fff.gca().plot(*periodogram(x, fs, nfft=fs * 3), c=cm[name.split()[1]])
plt.xlim(0, 80)
plt.ylim(0, 3e-11)
plt.show()
print(name)
time = np.arange(t, t + len(x)) / fs
color = cm[''.join([i for i in name.split()[1] if not i.isdigit()])]
ax1.plot(time, fft_filter(x, fs, (2, 45)), c=color, alpha=0.4)
ax1.plot(time, alpha[name], c=color)
t += len(x)
ax.plot([j_p], [pow.mean() / norm], 'o', c=color, markersize=10)
ax.errorbar([j_p], [pow.mean() / norm], yerr=pow.std() / norm, c=color, ecolor=color)
fb_x = np.hstack([[j] * len(pows) for j, (key, pows) in enumerate(powers.items()) if 'FB' in key])
fb_y = np.hstack([pows for key, pows in powers.items() if 'FB' in key]) / norm
sns.regplot(x=fb_x, y=fb_y, ax=ax, color=cm['FB'], scatter=False, truncate=True)
ax1.set_xlim(0, t / fs)
ax1.set_ylim(-40, 40)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=70)
ax.set_xticks(range(len(powers)))
ax.set_xticklabels(powers.keys())
ax.set_ylim(0, 3)
ax.set_xlim(-1, len(powers))
ax1.set_title('Day {}'.format(j_s + 1))
return fg
r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\ghosts-in-111_01-31_21-17-36\experiment_data.h5',
'405': r'D:\bipolar-test_02-03_21-26-20\experiment_data.h5',
'405-eeg': r'D:\new-lab-eeg-test_02-03_17-09-40\experiment_data.h5'
}
room = '104'
if room == '111-eeg':
import mne
a = mne.io.read_raw_brainvision(r'D:\bp-ghost-111-01-29-21-00-00\fff.vhdr')
x = a.get_data([a.ch_names.index('Aux1')])[0] / 1000 / 10
fs = 1000
else:
with h5py.File(rooms[room]) as f:
print(list(f.keys()), list(f['protocol1'].keys()))
from pynfb.postprocessing.utils import get_info
get_info(f, [])
data = [
f['protocol{}/raw_data'.format(k + 1)][:]
for k in range(len(f.keys()) - 3)
]
x = np.concatenate(data)[:500 * 4 * 60, 0]
fs = 500
fig, axes = plt.subplots(2, 1, sharex=True)
t = np.arange(len(x)) / fs / 60
axes[0].plot(t, x * 1000 * 1000)
axes[0].set_ylabel('Voltage [$\mu$V]')
axes[0].set_ylim(-50, 50)
axes[0].set_title('Room: {} Date: {} Start time: {}'.format(
room.split('-')[0], *rooms[room].split('\\')[-2].split('_')[-2:]))
'111-wed': r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\ghosts-in-111_01-31_21-17-36\experiment_data.h5',
'405': r'D:\bipolar-test_02-03_21-26-20\experiment_data.h5',
'405-eeg': r'D:\new-lab-eeg-test_02-03_17-09-40\experiment_data.h5'}
room = '104'
if room == '111-eeg':
import mne
a = mne.io.read_raw_brainvision(r'D:\bp-ghost-111-01-29-21-00-00\fff.vhdr')
x = a.get_data([a.ch_names.index('Aux1')])[0]/1000/10
fs = 1000
else:
with h5py.File(rooms[room]) as f:
print(list(f.keys()), list(f['protocol1'].keys()))
from pynfb.postprocessing.utils import get_info
get_info(f, [])
data = [f['protocol{}/raw_data'.format(k+1)][:] for k in range(len(f.keys())-3)]
x = np.concatenate(data)[:500*4*60, 0]
fs = 500
fig, axes = plt.subplots(2, 1, sharex=True)
t = np.arange(len(x))/fs/60
axes[0].plot(t, x*1000*1000)
axes[0].set_ylabel('Voltage [$\mu$V]')
axes[0].set_ylim(-50, 50)
axes[0].set_title('Room: {} Date: {} Start time: {}'.format(room.split('-')[0], *rooms[room].split('\\')[-2].split('_')[-2:]))
f, t, Sxx = signal.spectrogram(x, fs, scaling='spectrum')
ax = axes[1].pcolormesh(t/60, f, np.log10(Sxx**0.5), vmin=-7.1, vmax=-4.8, cmap='nipy_spectral')
axes[1].set_ylabel('Frequency [Hz]')
from pynfb.postprocessing.utils import get_info, fft_filter
import h5py
import numpy as np
import pandas as pd
import seaborn as sns
import pylab as plt
from pynfb.signal_processing.decompositions import ICADecomposition
from mne.viz import plot_topomap
from pynfb.inlets.montage import Montage
from scipy.signal import hilbert, welch
from scipy.stats import linregress, ttest_ind, ranksums, ttest_1samp
with h5py.File(r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\alpha-nfb-example_01-13_17-37-02\experiment_data.h5') as f:
fs, channels, p_names = get_info(f, ['A1', 'A2', 'AUX'])
channels = [ch.split('-')[0] for ch in channels]
mock_ind = [f['protocol{}'.format(k + 1)].attrs['mock_previous'] for k in range(len(p_names))]
data = [f['protocol{}/raw_data'.format(k + 1)][:] for k in range(len(p_names))]
signal = [f['protocol{}/signals_data'.format(k + 1)][:] for k in range(len(p_names))]
spat = f['protocol{}/signals_stats/Alpha/spatial_filter'.format(p_names.index('Baseline')+1)][:]
band = f['protocol{}/signals_stats/Alpha/bandpass'.format(p_names.index('Baseline') + 1)][:]
#print(p_names)
montage = Montage(channels)
print(mock_ind)
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)])
def plot_results(pilot_dir,
subj,
channel,
alpha_band=(9, 14),
theta_band=(3, 6),
drop_channels=None,
dc=False,
reject_alpha=True,
normalize_by='opened'):
drop_channels = drop_channels or []
cm = get_colors()
fg = plt.figure(figsize=(30, 6))
for j_s, experiment in enumerate(subj):
with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment,
'experiment_data.h5')) as f:
rejections, top_alpha, top_ica = load_rejections(
f, reject_alpha=reject_alpha)
fs, channels, p_names = get_info(f, drop_channels)
ch = channels.index(channel)
#plt.plot(fft_filter(f['protocol6/raw_data'][:, ch], fs, band=(3, 35)))
#plt.plot(fft_filter(np.dot(f['protocol6/raw_data'], rejections)[:, ch], fs, band=(3, 35)))
#plt.show()
#from scipy.signal import welch
#plt.plot(*welch(f['protocol1/raw_data'][:60*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol1/raw_data'][60*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][:30*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][30*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.legend(['Close', 'Open', 'Left', 'Right'])
#plt.show()
# collect powers
powers = OrderedDict()
raw = OrderedDict()
alpha = OrderedDict()
pow_theta = []
for j, name in enumerate(p_names):
pow, alpha_x, x = get_protocol_power(f,
j,
fs,
rejections,
ch,
alpha_band,
dc=dc)
if 'FB' in name:
pow_theta.append(
get_protocol_power(f,
j,
fs,
rejections,
ch,
theta_band,
dc=dc)[0].mean())
powers = add_data(powers, name, pow, j)
raw = add_data(raw, name, x, j)
alpha = add_data(alpha, name, alpha_x, j)
# plot rejections
n_tops = top_ica.shape[1] + top_alpha.shape[1]
for j_t in range(top_ica.shape[1]):
ax = fg.add_subplot(
4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1)
ax.set_xlabel('ICA{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [
label for label in labels if label not in drop_channels
]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_ica[:, j_t],
pos=pos,
axes=ax,
show=False)
for j_t in range(top_alpha.shape[1]):
ax = fg.add_subplot(
4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1 +
top_ica.shape[1])
ax.set_xlabel('CSP{}'.format(j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [
label for label in labels if label not in drop_channels
]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_alpha[:, j_t],
pos=pos,
axes=ax,
show=False)
# plot powers
if normalize_by == 'opened':
norm = powers['1. Opened'].mean()
elif normalize_by == 'beta':
norm = np.mean(pow_theta)
else:
print('WARNING: norm = 1')
print('norm', norm)
ax1 = fg.add_subplot(3, len(subj), j_s + 1)
ax = fg.add_subplot(3, len(subj), j_s + len(subj) + 1)
t = 0
for j_p, ((name, pow),
(name, x)) in enumerate(zip(powers.items(),
raw.items())):
if name == '2228. FB':
from scipy.signal import periodogram
fff = plt.figure()
fff.gca().plot(*periodogram(x, fs, nfft=fs * 3),
c=cm[name.split()[1]])
plt.xlim(0, 80)
plt.ylim(0, 3e-11)
plt.show()
print(name)
time = np.arange(t, t + len(x)) / fs
color = cm[''.join(
[i for i in name.split()[1] if not i.isdigit()])]
ax1.plot(time, fft_filter(x, fs, (2, 45)), c=color, alpha=0.4)
ax1.plot(time, alpha[name], c=color)
t += len(x)
ax.plot([j_p], [pow.mean() / norm],
'o',
c=color,
markersize=10)
ax.errorbar([j_p], [pow.mean() / norm],
yerr=pow.std() / norm,
c=color,
ecolor=color)
fb_x = np.hstack([[j] * len(pows)
for j, (key, pows) in enumerate(powers.items())
if 'FB' in key])
fb_y = np.hstack(
[pows for key, pows in powers.items() if 'FB' in key]) / norm
sns.regplot(x=fb_x,
y=fb_y,
ax=ax,
color=cm['FB'],
scatter=False,
truncate=True)
ax1.set_xlim(0, t / fs)
ax1.set_ylim(-40, 40)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=70)
ax.set_xticks(range(len(powers)))
ax.set_xticklabels(powers.keys())
ax.set_ylim(0, 3)
ax.set_xlim(-1, len(powers))
ax1.set_title('Day {}'.format(j_s + 1))
return fg
from pynfb.protocols.ssd.topomap_selector_ica import ICADialog
from PyQt5 import QtGui, QtWidgets
a = QtWidgets.QApplication([])
fig1, axes1 = plt.subplots(ncols=3)
for subj in range(3, 4):
for day in range(2, 5):
experiments = settings['subjects'][subj]
experiment = experiments[day]
reject = True
with h5py.File('{}\\{}\\{}'.format(settings['dir'], experiment,
'experiment_data.h5')) as f:
fs, channels, p_names = get_info(f, settings['drop_channels'])
rejection, alpha, ica = load_rejections(f, reject_alpha=False)
raw_before = OrderedDict()
raw_after = OrderedDict()
for j, name in enumerate(p_names):
if j < 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs,
rejection if reject else None)
raw_before = add_data_simple(raw_before, name, x)
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs,
rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
xx = np.concatenate([
return predicted_labels
if __name__ == '__main__':
np.random.seed(42)
# loading anp pre processing
import h5py
import pylab as plt
file = r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\BCI_Test_4_06-23_16-20-48\experiment_data.h5'
#file = r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\BCI_Test_2_1_06-22_17-01-07\experiment_data.h5'
labels_map = {'Open': 0, 'Right': 2, 'Left': 1}
with h5py.File(file) as f:
fs, ch_names, p_names = get_info(f, []) #['AUX', 'A1', 'A2', 'F4', 'Pz']
before = []
after = []
before_labels = []
after_labels = []
k = 0
for protocol in ['protocol{}'.format(j + 1) for j in range(len(f.keys()) - 3)]:
name = f[protocol].attrs['name']
if name in ['Right', 'Open', 'Left']:
data = f[protocol + '/raw_data'][:]
labels = np.ones(len(data), dtype=int) * labels_map[name]
if k < 9:
before.append(data)
before_labels.append(labels)
else:
after.append(data)
