Deconvolute

The yass.deconvolute module implements spikes deconvolution, yass.deconvolute.legacy contains and old algorithm that will be removed in the future

yass.deconvolute.run(spike_index, templates, recordings_filename='standarized.bin', function=<function legacy>)

Deconvolute spikes

Parameters:

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

A 2D array for all potential spikes whose first column indicates the spike time and the second column the principal channels. Or path to npy file

templates: numpy.ndarray (n_channels, waveform_size, n_templates), str

or pathlib.Path

A 3D array with the templates. Or path to npy file

output_directory: str, optional

Output directory (relative to CONFIG.data.root_folder) used to load the recordings to generate templates, defaults to tmp/

recordings_filename: str, optional

Recordings filename (relative to CONFIG.data.root_folder/ output_directory) used to draw the waveforms from, defaults to standarized.bin

Returns:

spike_train: numpy.ndarray (n_clear_spikes, 2)

A 2D array with the spike train, first column indicates the spike time and the second column the neuron ID

Examples

import logging
import numpy as np

import yass
from yass import preprocess
from yass import detect
from yass import cluster
from yass import templates
from yass import deconvolute

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', 'deconv-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)

(templates_, spike_train,
 groups, idx_good_templates) = templates.run(
    spike_train_clear, tmp_loc)

spike_train = deconvolute.run(spike_index_all, templates_)