Cluster

The yass.cluster module implements spike clustering algorithms, the yass.cluster.legacy implements two old clustering methods location and neigh_channels they are no longer used and will be removed soon when we add the new clustering algorithm

yass.cluster.run(*args, **kwargs)

Clustering step

Parameters:

scores: numpy.ndarray (n_spikes, n_features, n_channels), str or Path

3D array with the scores for the clear spikes, first simension is the number of spikes, second is the nymber of features and third the number of channels. Or path to a npy file

spike_index: numpy.ndarray (n_clear_spikes, 2), str or Path

2D array with indexes for spikes, first column contains the spike location in the recording and the second the main channel (channel whose amplitude is maximum). Or path to an npy file

output_directory: str, optional

Location to store/look for the generate spike train, relative to CONFIG.data.root_folder

if_file_exists: str, optional

One of ‘overwrite’, ‘abort’, ‘skip’. Control de behavior for the spike_train_cluster.npy. file If ‘overwrite’ it replaces the files if exists, if ‘abort’ it raises a ValueError exception if exists, if ‘skip’ it skips the operation if the file exists (and returns the stored file)

save_results: bool, optional

Whether to save spike train to disk (in CONFIG.data.root_folder/relative_to/spike_train_cluster.npy), defaults to False

Returns:

spike_train: (TODO add documentation)

Examples

import numpy as np
import logging

import yass
from yass import preprocess
from yass import detect
from yass import cluster

np.random.seed(0)

# configure logging module to get useful information
logging.basicConfig(level=logging.INFO)

# set yass configuration parameters
yass.set_config('config_sample.yaml', 'preprocess-example/')

standarized_path, standarized_params, whiten_filter = preprocess.run()

(spike_index_clear,
 spike_index_all) = detect.run(standarized_path,
                               standarized_params,
                               whiten_filter)

spike_train_clear, tmp_loc, vbParam = cluster.run(spike_index_clear)