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()
for j, name in enumerate(p_names):
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
raw_before = add_data(raw_before, name, x, j)
# plot raw data
ch_plot = ['C3', 'C4', 'P3', 'P4']#, 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=True)
print(axes)
#find median
x_all = []
for name, x in raw_before.items():
x_all.append(np.abs(hilbert(fft_filter(x, fs, (4, 8)))))
x_median = np.mean(np.concatenate(x_all), 0)
print(x_median)
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()
for j, name in enumerate(p_names):
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs,
rejection if reject else None)
raw_before = add_data(raw_before, name, x, j)
# plot raw data
ch_plot = ['C3', 'C4', 'P3', 'P4'] #, 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot), ncols=1, sharex=True, sharey=True)
print(axes)
#find median
x_all = []
for name, x in raw_before.items():
x_all.append(np.abs(hilbert(fft_filter(x, fs, (4, 8)))))
x_median = np.mean(np.concatenate(x_all), 0)
print(x_median)
# plot raw
t = 0
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
x_all = []
x_all_ica = []
for name, x in raw.items():
if 'Baseline' in name:
x_all.append(np.abs(hilbert(fft_filter(x, fs, band=mu_band))))
x_all_ica.append(
np.abs(
hilbert(np.dot(fft_filter(x, fs, band=mu_band), spatial))))
break
x_median = np.median(np.concatenate(x_all), 0)
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
if reject:
rejections = load_rejections(f, reject_alpha=True)[0]
else:
rejections = None
spatial = f['protocol15/signals_stats/left/spatial_filter'][:]
plot_topomap(spatial, ch_names_to_2d_pos(channels), axes=plt.gca(), show=False)
plt.savefig('alphaS{}_Day{}_spatial_filter'.format(subj, day+1))
mu_band = f['protocol15/signals_stats/left/bandpass'][:]
#mu_band = (12, 13)
max_gap = 1 / min(mu_band) * 2
min_sate_duration = max_gap * 2
raw = OrderedDict()
signal = OrderedDict()
for j, name in enumerate(p_names):
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejections)
raw = add_data(raw, name, x, j)
signal = add_data(signal, name, f['protocol{}/signals_data'.format(j + 1)][:], j)
del raw[list(raw.keys())[-1]]
# make csp:
if run_ica:
from PyQt5.QtWidgets import QApplication
ap = QApplication([])
all_keys = [key for key in raw.keys() if 'Left' in key or 'Right' in key or 'Close' in key or 'Open' in key]
first = all_keys[:len(all_keys)//2]
last = all_keys[len(all_keys)//2:]
tops = []
spats = []
fig1, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=False)
for j, data in enumerate([first, last]):
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
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'][:]
band = f['protocol15/signals_stats/left/bandpass'][:]
print('band:', band)
signals = OrderedDict()
raw = OrderedDict()
for j, name in enumerate(p_names):
if name != 'Bci':
x = np.dot(f['protocol{}/raw_data'.format(j + 1)][:],
spatial)
x = drop_outliers(x)
x = fft_filter(x, fs, band=band)
#x[x > 0.00005] = np.nan
signals = add_data(signals, name, x, j)
raw = add_data(
raw, name,
f['protocol{}/raw_data'.format(j + 1)][:], j)
norm_coeff = np.std(signals['16. Baseline'])
fb_vars = []
for fb_name in ['18. FB', '20. FB', '22. FB']:
for fb_part in np.split(
signals[fb_name],
range(0, len(signals[fb_name]), 2 * fs))[1:-1]:
fb_vars.append(fb_part.std())
fb_vars = np.array(fb_vars) / norm_coeff
#sns.regplot(np.arange(len(fb_vars)), fb_vars)
slope, intercept, r_value, p_value, std_err = stats.linregress(
np.arange(len(fb_vars)), fb_vars)