def __init__(self, fn, channels, to_freq, montage_channels, montage, idx=1, csp_time=[0, 0.3], a_time=0.5):
self.data = sp.signalParser(fn)
self.tags = self.data.get_p300_tags(idx=idx, samples=False)
signal = self.data.prep_signal(to_freq, channels, montage_channels, montage)
signal2 = np.zeros(signal.shape)
self.fs = to_freq
signal2 = np.zeros(signal.shape)
self.wrong_tags = self.data.get_p300_tags(idx=idx, rest=True, samples=False)
artifacts_data = np.zeros([len(channels), self.fs * a_time, len(self.tags)])
for i, p in enumerate(self.tags):
artifacts_data[..., i] = signal[
:, p * self.data.sampling_frequency : (p + a_time) * self.data.sampling_frequency
]
self.a_features, self.bands = artifactsCalibration(artifacts_data, self.data.sampling_frequency)
signal2 = np.zeros(signal.shape)
self.fs = to_freq
self.channels = channels
self.idx = idx
b, a = ss.butter(3, 2 * 1.0 / self.fs, btype="high")
b_l, a_l = ss.butter(3, 20.0 * 2 / self.fs, btype="low")
for e in xrange(len(self.channels)):
tmp = filtfilt(b, a, signal[e, :])
signal2[e, :] = filtfilt(b_l, a_l, tmp)
self.signal_original = signal2
self.t1, self.t2 = self.show_mean(csp_time, "Cz", dont_plot=False)
P, vals = self.train_csp(signal2, [self.t1, self.t2])
self.P = P
self.signal = np.dot(P[:, 0], signal2)
def __init__(self,
fn,
channels,
to_freq,
montage_channels,
montage,
idx=1,
csp_time=[0, 0.3],
a_time=0.5):
self.data = sp.signalParser(fn)
self.tags = self.data.get_p300_tags(idx=idx, samples=False)
signal = self.data.prep_signal(to_freq, channels, montage_channels,
montage)
signal2 = np.zeros(signal.shape)
self.fs = to_freq
signal2 = np.zeros(signal.shape)
self.wrong_tags = self.data.get_p300_tags(idx=idx,
rest=True,
samples=False)
artifacts_data = np.zeros(
[len(channels), self.fs * a_time,
len(self.tags)])
for i, p in enumerate(self.tags):
artifacts_data[...,
i] = signal[:, p *
self.data.sampling_frequency:(p +
a_time) *
self.data.sampling_frequency]
self.a_features, self.bands = artifactsCalibration(
artifacts_data, self.data.sampling_frequency)
signal2 = np.zeros(signal.shape)
self.fs = to_freq
self.channels = channels
self.idx = idx
b, a = ss.butter(3, 2 * 1.0 / self.fs, btype='high')
b_l, a_l = ss.butter(3, 20.0 * 2 / self.fs, btype='low')
for e in xrange(len(self.channels)):
tmp = filtfilt(b, a, signal[e, :])
signal2[e, :] = filtfilt(b_l, a_l, tmp)
self.signal_original = signal2
self.t1, self.t2 = self.show_mean(csp_time, 'Cz', dont_plot=False)
P, vals = self.train_csp(signal2, [self.t1, self.t2])
self.P = P
self.signal = np.dot(P[:, 0], signal2)
def run(in_file, use_channels, ignore_channels, montage, montage_channels, plot_stats=True,
context=ctx.get_dummy_context('UgmBlinkingConnection')):
logger = context['logger']
mgr = read_manager.ReadManager(
in_file+'.obci.xml',
in_file+'.obci.raw',
in_file+'.obci.tag')
to_frequency = int(float(mgr.get_param("sampling_frequency")))
if use_channels is None:
use_channels = mgr.get_param('channels_names')
if ignore_channels is not None:
for i in ignore_channels:
try:
use_channels.remove(i)
except:
pass
exp_info = mgr.get_tags('experimentInfo')[0]['desc']
to_signal = float(exp_info['target_time'])
freqs = [int(i) for i in exp_info['all_freqs'].split(';')]
dec_count = 4
data = sp.signalParser(in_file+'.obci')
train_tags = data.get_train_tags(trial_separator_name='diodes', screen=True)
logger.info("Run csp with: use_channels: "+str(use_channels)+" motage: "+str(montage)+" montage_channels: "+str(montage_channels))
q = csp.modCSP(in_file+'.obci', freqs, use_channels, montage, montage_channels)
q.start_CSP(to_signal, to_frequency, baseline = False, filt='cheby', method = 'regular', train_tags = train_tags)#liczenie CSP
time = pylab.linspace(1, to_signal, 7)
t1, t2 = q.time_frequency_selection(to_frequency, train_tags, time=time, frequency_no=dec_count, plt=False)
logger.info("Got times t1: "+str(t1)+ " and t2: "+str(t2))
value, mu, sigma, means, stds, out_top, out_bottom = q.count_stats(to_signal, to_frequency, train_tags, plt=plot_stats)#Liczenie statystyk
logger.info("For freqs: "+str(freqs))
logger.info("Got means: "+str(means))
pairs = zip(means, freqs)
pairs.sort(reverse=True)
logger.info("Sorted: "+str(pairs))
best = [i[1] for i in pairs]
best_means = [i[0] for i in pairs]
logger.info("Best freqs: "+str(best))
logger.info("Finished CSP with stats:")
logger.info(str(value) + " / " + str(mu) + " / " + str(sigma))
logger.info("And q:")
logger.info(str(q))
tmp = best[:dec_count]
best_freqs = [tmp[0],0,tmp[1],0,
0,tmp[2],0,tmp[3]]
best_freqs = [str(i) for i in best_freqs]
logger.info("freqs:"+str(freqs))
d = {'value': value,
'mu': mu,
'sigma': sigma,
'q' : q,
'freqs':';'.join(best_freqs),
'all_freqs':';'.join([str(i) for i in best]),
'all_means':';'.join([str(i) for i in best_means]),
'dec_count':dec_count,
'buffer':t2,
'use_channels':';'.join(use_channels),
'montage':montage,
'montage_channels':';'.join(montage_channels),
'out_top':out_top,
'out_bottom':out_bottom
}
return d
def run(in_file,
use_channels,
ignore_channels,
montage,
montage_channels,
dec_count,
plot_stats=True,
context=ctx.get_dummy_context('UgmBlinkingConnection')):
logger = context['logger']
mgr = read_manager.ReadManager(in_file + '.obci.xml',
in_file + '.obci.raw',
in_file + '.obci.tag')
to_frequency = int(float(mgr.get_param("sampling_frequency")))
if use_channels is None:
use_channels = mgr.get_param('channels_names')
if ignore_channels is not None:
for i in ignore_channels:
try:
use_channels.remove(i)
except:
pass
exp_info = mgr.get_tags('experimentInfo')[0]['desc']
to_signal = float(exp_info['target_time'])
freqs = [int(i) for i in exp_info['all_freqs'].split(';')]
data = sp.signalParser(in_file + '.obci')
train_tags = data.get_train_tags(trial_separator_name='diodes',
screen=True)
logger.info("Run csp with: use_channels: " + str(use_channels) +
" motage: " + str(montage) + " montage_channels: " +
str(montage_channels))
q = csp.modCSP(in_file + '.obci', freqs, use_channels, montage,
montage_channels)
q.start_CSP(to_signal,
to_frequency,
baseline=False,
filt='cheby',
method='regular',
train_tags=train_tags) #liczenie CSP
time = pylab.linspace(1, to_signal, 7)
t1, t2 = q.time_frequency_selection(to_frequency,
train_tags,
time=time,
frequency_no=dec_count,
plt=False)
logger.info("Got times t1: " + str(t1) + " and t2: " + str(t2))
logger.info("Set buffer time:" + str(t2))
value, mu, sigma, means, stds, out_top, out_bottom = q.count_stats(
to_signal, to_frequency, train_tags,
plt=plot_stats) #Liczenie statystyk
logger.info("For freqs: " + str(freqs))
logger.info("Got means: " + str(means))
pairs = zip(means, freqs)
pairs.sort(reverse=True)
logger.info("Sorted: " + str(pairs))
best = [i[1] for i in pairs]
best_means = [i[0] for i in pairs]
logger.info("Best freqs: " + str(best))
logger.info("dec_count: " + str(dec_count))
logger.info("freqs: " + str(best[:dec_count]))
logger.info("Finished CSP with stats:")
logger.info(str(value) + " / " + str(mu) + " / " + str(sigma))
logger.info("And q:")
logger.info(str(q))
d = {
'value': value,
'mu': mu,
'sigma': sigma,
'q': q,
'freqs': ';'.join([str(i) for i in best[:dec_count]]),
'all_freqs': ';'.join([str(i) for i in best]),
'all_means': ';'.join([str(i) for i in best_means]),
'dec_count': dec_count,
'buffer': t2,
'use_channels': ';'.join(use_channels),
'montage': montage,
'montage_channels': ';'.join(montage_channels),
'out_top': out_top,
'out_bottom': out_bottom
}
return d