#NEWLINE# def _init_data( self ) : #NEWLINE# #INDENT# self . cache_dir = self . config_dir #NEWLINE# self . n_samples_waveforms = 31 #NEWLINE# self . n_samples_t = 20000 #NEWLINE# self . n_channels = 11 #NEWLINE# self . n_clusters = 4 #NEWLINE# self . n_spikes_per_cluster = 200 #NEWLINE# n_spikes_total = ( self . n_clusters * self . n_spikes_per_cluster ) #NEWLINE# n_features_per_channel = 4 #NEWLINE# self . n_channels = self . n_channels #NEWLINE# self . n_spikes = n_spikes_total #NEWLINE# self . sample_rate = 20000.0 #NEWLINE# self . duration = ( self . n_samples_t / float ( self . sample_rate ) ) #NEWLINE# self . spike_times = np . arange ( 0 , self . duration , ( 5000.0 / ( self . sample_rate * self . n_spikes_per_cluster ) ) ) #NEWLINE# self . spike_clusters = np . repeat ( np . arange ( self . n_clusters ) , self . n_spikes_per_cluster ) #NEWLINE# assert ( len ( self . spike_times ) == len ( self . spike_clusters ) ) #NEWLINE# self . cluster_ids = np . unique ( self . spike_clusters ) #NEWLINE# self . channel_positions = staggered_positions ( self . n_channels ) #NEWLINE# sc = self . spike_clusters #NEWLINE# self . spikes_per_cluster = ( lambda c : _spikes_in_clusters ( sc , [ c ] ) ) #NEWLINE# self . spike_count = ( lambda c : len ( self . spikes_per_cluster ( c ) ) ) #NEWLINE# self . n_features_per_channel = n_features_per_channel #NEWLINE# self . cluster_groups = { c : None for c in range ( self . n_clusters ) } #NEWLINE# self . all_traces = artificial_traces ( self . n_samples_t , self . n_channels ) #NEWLINE# self . all_masks = artificial_masks ( n_spikes_total , self . n_channels ) #NEWLINE# self . all_waveforms = artificial_waveforms ( n_spikes_total , self . n_samples_waveforms , self . n_channels ) #NEWLINE# self . all_features = artificial_features ( n_spikes_total , self . n_channels , self . n_features_per_channel )