def _init_data(self): self.cache_dir = self.config_dir self.n_samples_waveforms = 31 self.n_samples_t = 20000 self.n_channels = 11 self.n_clusters = 4 self.n_spikes_per_cluster = 50 n_spikes_total = self.n_clusters * self.n_spikes_per_cluster n_features_per_channel = 4 self.n_channels = self.n_channels self.n_spikes = n_spikes_total self.sample_rate = 20000. self.duration = self.n_samples_t / float(self.sample_rate) self.spike_times = np.arange(0, self.duration, 100. / self.sample_rate) self.spike_clusters = np.repeat(np.arange(self.n_clusters), self.n_spikes_per_cluster) assert len(self.spike_times) == len(self.spike_clusters) self.cluster_ids = np.unique(self.spike_clusters) self.channel_positions = staggered_positions(self.n_channels) sc = self.spike_clusters self.spikes_per_cluster = lambda c: _spikes_in_clusters(sc, [c]) self.spike_count = lambda c: len(self.spikes_per_cluster(c)) self.n_features_per_channel = n_features_per_channel self.cluster_groups = {c: None for c in range(self.n_clusters)} self.all_traces = artificial_traces(self.n_samples_t, self.n_channels) self.all_masks = artificial_masks(n_spikes_total, self.n_channels) self.all_waveforms = artificial_waveforms(n_spikes_total, self.n_samples_waveforms, self.n_channels) self.all_features = artificial_features(n_spikes_total, self.n_channels, self.n_features_per_channel)
def _init_data(self): self.cache_dir = self.config_dir self.n_samples_waveforms = 31 self.n_samples_t = 20000 self.n_channels = 11 self.n_clusters = 4 self.n_spikes_per_cluster = 200 n_spikes_total = self.n_clusters * self.n_spikes_per_cluster n_features_per_channel = 4 self.n_channels = self.n_channels self.n_spikes = n_spikes_total self.sample_rate = 20000. self.duration = self.n_samples_t / float(self.sample_rate) self.spike_times = np.arange( 0, self.duration, 5000. / (self.sample_rate * self.n_spikes_per_cluster)) self.spike_clusters = np.repeat(np.arange(self.n_clusters), self.n_spikes_per_cluster) assert len(self.spike_times) == len(self.spike_clusters) self.cluster_ids = np.unique(self.spike_clusters) self.channel_positions = staggered_positions(self.n_channels) self.channel_order = np.arange(self.n_channels) sc = self.spike_clusters self.spikes_per_cluster = lambda c: _spikes_in_clusters(sc, [c]) self.spike_count = lambda c: len(self.spikes_per_cluster(c)) self.n_features_per_channel = n_features_per_channel self.cluster_groups = {c: None for c in range(self.n_clusters)} self.all_traces = artificial_traces(self.n_samples_t, self.n_channels) self.all_masks = artificial_masks(n_spikes_total, self.n_channels) self.all_waveforms = artificial_waveforms(n_spikes_total, self.n_samples_waveforms, self.n_channels) self.all_features = artificial_features(n_spikes_total, self.n_channels, self.n_features_per_channel)
def get_waveforms(cluster_id): return Bunch( data=artificial_waveforms(10, 20, nc), channel_ids=np.arange(nc), channel_positions=staggered_positions(nc), )
def waveforms(n_spikes, n_samples, n_channels): yield artificial_waveforms(n_spikes, n_samples, n_channels)
def get_waveforms(cluster_id): return Bunch( data=artificial_waveforms(10, 20, nc), channel_ids=np.arange(nc), channel_positions=staggered_positions(nc) )