Detect

The yass.detect module implements spike detectors, nnet.py contains a Neural Network detector and threshold.py contains the threshold detector, both of them use the YASS batch processor (yass.batch)

yass.detect.run(standarized_path, standarized_params, whiten_filter, if_file_exists='skip', save_results=False, function=<function run>)

Execute detect step

Parameters:

standarized_path: str or pathlib.Path

Path to standarized data binary file

standarized_params: dict, str or pathlib.Path

Dictionary with standarized data parameters or path to a yaml file

whiten_filter: numpy.ndarray, str or pathlib.Path

Whiten matrix or path to a npy file

if_file_exists: str, optional

One of ‘overwrite’, ‘abort’, ‘skip’. Control de behavior for every generated file. If ‘overwrite’ it replaces the files if any exist, if ‘abort’ it raises a ValueError exception if any file exists, if ‘skip’ if skips the operation if any file exists

save_results: bool, optional

Whether to save results to disk, defaults to False

Returns:

clear_scores: numpy.ndarray (n_spikes, n_features, n_channels)

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

spike_index_clear: numpy.ndarray (n_clear_spikes, 2)

2D array with indexes for clear spikes, first column contains the spike location in the recording and the second the main channel (channel whose amplitude is maximum)

spike_index_all: numpy.ndarray (n_collided_spikes, 2)

2D array with indexes for all spikes, first column contains the spike location in the recording and the second the main channel (channel whose amplitude is maximum)

Notes

Running the preprocessor will generate the followiing files in CONFIG.data.root_folder/output_directory/ (if save_results is True):

• spike_index_clear.npy - Same as spike_index_clear returned
• spike_index_all.npy - Same as spike_index_collision returned
• rotation.npy - Rotation matrix for dimensionality reduction
• scores_clear.npy - Scores for clear spikes

Threshold detector runs on CPU, neural network detector runs CPU and GPU, depending on how tensorflow is configured.

Examples

"""
Detecting spikes
"""
import logging

import yass
from yass import preprocess
from yass import detect

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

# set yass configuration parameters
yass.set_config('config.yaml', 'example-detect')

# run preprocessor
standarized_path, standarized_params, whiten_filter = preprocess.run()

# run detection
clear, collision = detect.run(standarized_path,
                              standarized_params,
                              whiten_filter,
                              if_file_exists='overwrite')