def export_spikes(dirname, array_idx, chan_grp):
print('Exporting ch %d' % chan_grp)
data = {
'array': [],
'electrode': [],
'cell': [],
'segment': [],
'time': []
}
array = cfg['arrays'][array_idx]
dataio = DataIO(dirname=dirname, ch_grp=chan_grp)
catalogue = dataio.load_catalogue(chan_grp=chan_grp)
dataio._open_processed_data(ch_grp=chan_grp)
clusters = catalogue['clusters']
for seg_num in range(dataio.nb_segment):
spikes = dataio.get_spikes(seg_num=seg_num, chan_grp=chan_grp)
spike_labels = spikes['cluster_label'].copy()
for l in clusters:
mask = spike_labels == l['cluster_label']
spike_labels[mask] = l['cell_label']
spike_indexes = spikes['index']
for (index, label) in zip(spike_indexes, spike_labels):
if label >= 0:
data['array'].append(array)
data['electrode'].append(chan_grp)
data['cell'].append(label)
data['segment'].append(seg_num)
data['time'].append(index)
dataio.flush_processed_signals(seg_num=seg_num, chan_grp=chan_grp)
df = pd.DataFrame(
data, columns=['array', 'electrode', 'cell', 'segment', 'time'])
df.to_csv(os.path.join(dirname, '%s_%d_spikes.csv' % (array, chan_grp)),
index=False)
