def restore_online_signal(h5_dataset,
signal_name,
band=None,
spatial_filter=None,
smoothing_factor=0.999):
df, fs, channels, p_names = load_data(h5_dataset)
n_channels = len(channels)
with h5py.File(h5_dataset) as f:
last_block = len(df.block_number.unique())
if spatial_filter is None:
spatial_filter = f['protocol{}/signals_stats/Signal/spatial_filter'
.format(last_block)][:]
if band is None:
band = f['protocol{}/signals_stats/Signal/bandpass'.format(
last_block)][:]
xml_settings = load_xml_str_from_hdf5_dataset(h5_dataset, 'settings.xml')
params = xml_file_to_params(xml_settings)
signals_params_list = params['vSignals']['DerivedSignal']
signals_names = [s['sSignalName'] for s in signals_params_list]
signal = signals_params_list[signals_names.index(signal_name)]
signal['fBandpassLowHz'], signal['fBandpassHighHz'] = band
signal['sTemporalFilterType'] = 'butter'
signal['fSmoothingFactor'] = smoothing_factor
signal = DerivedSignal.from_params(0, fs, n_channels, channels, signal,
spatial_filter)
time_series = signal.update(df[channels].values)
return signal, time_series
def load_signal(filename, channels_labels):
signal = xml_file_to_odict(filename)
default = vectors_defaults['vSignals']['DerivedSignal'][0].copy()
for key, value in default.items():
default[key] = signal.get(key, value)
signal = default
if isinstance(signal['SpatialFilterMatrix'], str):
if signal['SpatialFilterMatrix'] == '':
spatial_filter = None
else:
spatial_filter = read_spatial_filter(signal['SpatialFilterMatrix'],
channels_labels)
elif isinstance(signal['SpatialFilterMatrix'], list):
spatial_filter = array(signal['SpatialFilterMatrix']).astype(float)
else:
raise TypeError(
'\'SpatialFilterMatrix\' must be string or list (vector)')
s = DerivedSignal(
bandpass_high=signal['fBandpassHighHz'],
bandpass_low=signal['fBandpassLowHz'],
name=signal['sSignalName'],
n_channels=len(channels_labels),
spatial_filter=spatial_filter,
disable_spectrum_evaluation=signal['bDisableSpectrumEvaluation'],
n_samples=signal['fFFTWindowSize'],
smoothing_factor=signal['fSmoothingFactor'])
return s
def restore_online_signal(h5_dataset, signal_name, band=None, spatial_filter=None, smoothing_factor=0.999):
df, fs, channels, p_names = load_data(h5_dataset)
n_channels = len(channels)
with h5py.File(h5_dataset) as f:
last_block = len(df.block_number.unique())
if spatial_filter is None:
spatial_filter = f['protocol{}/signals_stats/Signal/spatial_filter'.format(last_block)][:]
if band is None:
band = f['protocol{}/signals_stats/Signal/bandpass'.format(last_block)][:]
xml_settings = load_xml_str_from_hdf5_dataset(h5_dataset, 'settings.xml')
params = xml_file_to_params(xml_settings)
signals_params_list = params['vSignals']['DerivedSignal']
signals_names = [s['sSignalName'] for s in signals_params_list]
signal = signals_params_list[signals_names.index(signal_name)]
signal['fBandpassLowHz'], signal['fBandpassHighHz'] = band
signal['sTemporalFilterType'] = 'butter'
signal['fSmoothingFactor'] = smoothing_factor
signal = DerivedSignal.from_params(0, fs, n_channels, channels, signal, spatial_filter)
time_series = signal.update(df[channels].values)
return signal, time_series
labels_, fs_ = get_lsl_info_from_xml(f['stream_info.xml'][0])
print(labels_)
channels = [
label for label in labels_ if label not in ['A1', 'A2', 'AUX']
]
print(labels_)
pz_index = channels.index('Pz')
raw = raw - np.dot(raw[:, [pz_index]], np.ones((1, raw.shape[1])))
del channels[pz_index]
raw = raw[:, np.arange(raw.shape[1]) != pz_index]
signal = DerivedSignal(ind=0,
name='Signal',
bandpass_low=9,
bandpass_high=14,
spatial_filter=np.array([0]),
n_channels=raw.shape[1])
w = SignalsSSDManager([signal],
raw,
ch_names_to_2d_pos(channels),
channels,
None,
None, [],
sampling_freq=fs_)
w.exec_()
rejections = signal.rejections.get_list()
new_rejections[experiment] = rejections
with open(new_rejections_file, 'wb') as pkl:
pickle.dump(new_rejections, pkl)
#channels = [b[0] for b in mat_file['EEGchanslabels'][0]]
#print(np.shape(channels))
#print(np.shape(x))
#print(np.shape(marks))
channels = [
'Fc1', 'Fc3', 'Fc5', 'C1', 'C3', 'C5', 'Cp1', 'Cp3', 'Cp5', 'Cz', 'Pz',
'Cp2', 'Cp4', 'Cp6', 'C2', 'C4', 'C6', 'Fc2', 'Fc4', 'Fc6'
]
n_ch = len(channels)
from pynfb.signals import CompositeSignal
signals = [
DerivedSignal(ind=k,
source_freq=500,
name='Signal' + str(k),
bandpass_low=0 + k,
bandpass_high=1 + 10 * k,
spatial_filter=np.array([k]),
n_channels=n_ch) for k in range(3)
]
signals += [CompositeSignal(signals, '', 'Composite', 3, fs=500)]
signals += [BCISignal(500, channels, 'bci', n_ch)]
app = QtGui.QApplication([])
x = np.random.randn(5000, n_ch)
from pynfb.widgets.helpers import ch_names_to_2d_pos
#x[2500:3000, channels.index('Cz')] /= 50
x = x[:50000]
#marks = marks[:50000]
from pynfb.signals import DerivedSignal, CompositeSignal
from pynfb.io.hdf5 import save_signals
signals = [DerivedSignal(name='One'),
DerivedSignal(name='Two')]
signals += [CompositeSignal(signals, 'One+Two', name='Three')]
save_signals('signals_info.hdf5', signals, group_name='protocol1')