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 vendor.nfb.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
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
from vendor.nfb.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 vendor.nfb.pynfb.signal_processing.decompositions import ICADecomposition
from mne.viz import plot_topomap
from vendor.nfb.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)])
# load raw
from vendor.nfb.pynfb.widgets.helpers import ch_names_to_2d_pos
mock = False
dir_ = r'D:\bci_nfb_bci\bci_nfb_bci'
with open(dir_ + '\\info.json', 'r') as f:
settings = loads(f.read())
subj = 0
day = 2
experiment = settings['subjects'][subj][day]
with h5py.File('{}\\{}\\{}'.format(dir_, experiment,
'experiment_data.h5')) as f:
fs, channels, p_names = get_info(f, settings['drop_channels'])
spatial = f['protocol15/signals_stats/left/spatial_filter'][:]
mu_band = f['protocol15/signals_stats/left/bandpass'][:]
max_gap = 1 / min(mu_band) * 2
min_sate_duration = max_gap * 2
raw = OrderedDict()
signal = OrderedDict()
for j, name in enumerate(p_names):
if name != 'Bci':
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs)
raw = add_data(raw, name, x, j)
signal = add_data(signal, name,
f['protocol{}/signals_data'.format(j + 1)][:], j)
ch_plot = ['C3', 'P3', 'ICA'] # , 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=False)
# find median
from vendor.nfb.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 vendor.nfb.pynfb.signal_processing.decompositions import ICADecomposition
from mne.viz import plot_topomap
from vendor.nfb.pynfb.inlets.montage import Montage
from scipy.signal import hilbert
with h5py.File(
r'C:\Users\Nikolai\PycharmProjects\nfb\pynfb\results\bci-example_12-28_17-57-26\experiment_data.h5'
) as f:
fs, channels, p_names = get_info(f, [])
channels = [ch.split('-')[0] for ch in channels]
data = [
f['protocol{}/raw_data'.format(k + 1)][:] for k in range(len(p_names))
]
print(p_names)
montage = Montage(channels)
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)])
df['times'] = np.concatenate([np.arange(len(d)) for d in data])
df = df[df['block_name'].isin(['Legs', 'Rest', 'Left', 'Right'])]
#
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)
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
'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 vendor.nfb.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]')
