def determine_sensors(self): #TODELAY: change to only taking filename? maybe more # clarity where file is opened all_sensor_names = self.get_all_sensors(self.filename, pattern=None) if self.load_sensor_names is None: # if no sensor names given, take all EEG-chans EEG_sensor_names = all_sensor_names EEG_sensor_names = filter(lambda s: not s.startswith('BIP'), EEG_sensor_names) EEG_sensor_names = filter(lambda s: not s.startswith('E'), EEG_sensor_names) EEG_sensor_names = filter(lambda s: not s.startswith('Microphone'), EEG_sensor_names) EEG_sensor_names = filter(lambda s: not s.startswith('Breath'), EEG_sensor_names) EEG_sensor_names = filter(lambda s: not s.startswith('GSR'), EEG_sensor_names) assert (len(EEG_sensor_names) == 128 or len(EEG_sensor_names) == 64 or len(EEG_sensor_names) == 32 or len(EEG_sensor_names) == 16), ( "Recheck this code if you have different sensors...") # sort sensors topologically to allow networks to exploit topology # this is kpe there to ensure reproducibility, # rerunning of old results only self.load_sensor_names = sort_topologically(EEG_sensor_names) chan_inds = self.determine_chan_inds(all_sensor_names, self.load_sensor_names) return chan_inds, self.load_sensor_names
def __init__(self, signal_processor, sensor_names='all', axes=('b', 'c', 0, 1), sort_topological=True, end_marker_def=None, marker_cutter=None): # sort sensors topologically to allow networks to exploit topology if (sensor_names is not None) and (sensor_names != 'all') and sort_topological: sensor_names = sort_topologically(sensor_names) self.__dict__.update(locals()) del self.self
def preprocess_test_set(self): if self.sensor_names is not None: self.sensor_names = sort_topologically(self.sensor_names) self.test_cnt = select_channels(self.test_cnt, self.sensor_names) if self.set_cz_to_zero is True: self.test_cnt = set_channel_to_zero(self.test_cnt, 'Cz') if self.resample_fs is not None: self.test_cnt = resample_cnt(self.test_cnt, newfs=self.resample_fs) if self.common_average_reference is True: self.test_cnt = common_average_reference_cnt(self.test_cnt) if self.standardize_cnt is True: self.test_cnt = exponential_standardize_cnt(self.test_cnt)
def preprocess_test_set(self): if self.sensor_names is not None: self.sensor_names = sort_topologically(self.sensor_names) self.test_cnt = select_channels(self.test_cnt, self.sensor_names) if self.set_cz_to_zero is True: self.test_cnt = set_channel_to_zero(self.test_cnt, 'Cz') if self.resample_fs is not None: self.test_cnt = resample_cnt(self.test_cnt, newfs=self.resample_fs) if self.common_average_reference is True: self.test_cnt = common_average_reference_cnt(self.test_cnt) if self.standardize_cnt is True: self.test_cnt = exponential_standardize_cnt(self.test_cnt)
def __init__( self, signal_processor, sensor_names="all", axes=("b", "c", 0, 1), sort_topological=True, end_marker_def=None, marker_cutter=None, ): # sort sensors topologically to allow networks to exploit topology if (sensor_names is not None) and (sensor_names != "all") and sort_topological: sensor_names = sort_topologically(sensor_names) self.__dict__.update(locals()) del self.self
def __init__(self, signal_processor, sensor_names='all', limits=None, start=None, stop=None, axes=('b', 'c', 0, 1), unsupervised_preprocessor=None, sort_topological=True): # sort sensors topologically to allow networks to exploit topology if (sensor_names is not None) and (sensor_names is not 'all') and sort_topological: sensor_names = sort_topologically(sensor_names) self.__dict__.update(locals()) del self.self
def determine_sensors(self): #TODELAY: change to only taking filename? maybe more # clarity where file is opened all_sensor_names = self.get_all_sensors(self.filename, pattern=None) if self.load_sensor_names is None: # if no sensor names given, take all EEG-chans EEG_sensor_names = filter(lambda s: not s.startswith('E'), all_sensor_names) EEG_sensor_names = filter(lambda s: not s.startswith('Microphone'), EEG_sensor_names) EEG_sensor_names = filter(lambda s: not s.startswith('Breath'), EEG_sensor_names) EEG_sensor_names = filter(lambda s: not s.startswith('GSR'), EEG_sensor_names) assert (len(EEG_sensor_names) == 128 or len(EEG_sensor_names) == 64 or len(EEG_sensor_names) == 32 or len(EEG_sensor_names) == 16), ( "Recheck this code if you have different sensors...") # sort sensors topologically to allow networks to exploit topology self.load_sensor_names = sort_topologically(EEG_sensor_names) chan_inds = self.determine_chan_inds(all_sensor_names, self.load_sensor_names) return chan_inds, self.load_sensor_names