def main():
# removed reference to this file in config, it is not necessary
# TODO: let the user specify the name through an option
output_file = 'spike_train.csv'
# configure logging module to get useful information
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
parser = argparse.ArgumentParser(description='Run YASS.')
parser.add_argument('config', type=str,
help='Path to configuration file')
args = parser.parse_args()
cfg = yass.Config.from_yaml(args.config)
pp = Preprocessor(cfg)
score, spike_index_clear, spike_index_collision = pp.process()
mp = Mainprocessor(cfg, score, spike_index_clear, spike_index_collision)
spikeTrain_clear, spike_index_collision = mp.mainProcess()
dc = Deconvolution(cfg, np.transpose(mp.templates,[1,0,2]), spike_index_collision)
spikeTrain_col = dc.fullMPMU()
spikeTrain = np.concatenate((spikeTrain_col, spikeTrain_clear))
idx_sort = np.argsort(spikeTrain[:, 0])
spikeTrain = spikeTrain[idx_sort]
idx_keep = np.zeros(spikeTrain.shape[0],'bool')
for k in range(mp.templates.shape[2]):
idx_c = np.where(spikeTrain[:,1] == k)[0]
idx_keep[idx_c[np.concatenate(([True], np.diff(spikeTrain[idx_c,0]) > 1))]] = 1
spikeTrain = spikeTrain[idx_keep]
path_to_file = os.path.join(cfg.data.root_folder, output_file)
np.savetxt(path_to_file, spikeTrain, fmt='%i, %i')
print('Done, spike train saved in: {}'.format(path_to_file))
# set yass configuration parameters
#set_config(args.config)
#CONFIG = read_config()
# run preprocessor
#score, spike_index_clear, spike_index_collision = preprocess.run()
# run processor
#spike_train_clear, templates, spike_index_collision = process.run(score,
# spike_index_clear, spike_index_collision)
# run deconvolution
#spike_train = deconvolute.run(spike_train_clear, templates,
# spike_index_collision)
# path_to_file = os.path.join(cfg.data.root_folder, output_file)
#np.savetxt(path_to_file, spike_train, fmt='%i, %i')
#logger.info('Done, spike train saved in: {}'.format(path_to_file))
def getCleanSpikeTrain(config):
"""
Threshold detection for extracting clean templates from the raw recording if groundtruth is not available
Parameters:
-----------
config: configuration object
configuration object containing the parameters for making the training data.
Returns:
-----------
spikeTrain: np.array
[number of spikes, 2] first column corresponds to spike time; second column corresponds to cluster id.
"""
config.detctionMethod = 'threshold'
config.doWhitening = 0
config.doDeconv = 0
pp = Preprocessor(config)
score, clr_idx, spt = pp.process()
mp = Mainprocessor(config, score, clr_idx, spt)
mp.mainProcess()
return mp.spikeTrain
def test_process_1k(path_to_config_1k):
cfg = yass.Config.from_yaml(path_to_config_1k)
pp = Preprocessor(cfg)
score, clr_idx, spt = pp.process()
mp = Mainprocessor(cfg, score, clr_idx, spt)
spike_train, spt_left = mp.mainProcess()
def test_deconvolute(path_to_config):
cfg = yass.Config.from_yaml(path_to_config)
pp = Preprocessor(cfg)
score, clr_idx, spt = pp.process()
mp = Mainprocessor(cfg, score, clr_idx, spt)
spike_train, spt_left = mp.mainProcess()
dc = Deconvolution(cfg, np.transpose(mp.templates, [1, 0, 2]), spt_left)
dc.fullMPMU()
# Note: we are working on a improved version Deconvolution, this old
# pipeline will be removed in the near future. However, migrating to the new
# pipeline requires minimum code changes
import numpy as np
import yass
from yass.preprocessing import Preprocessor
from yass.mainprocess import Mainprocessor
from yass.deconvolute import Deconvolution
cfg = yass.Config.from_yaml('tests/config_nnet.yaml')
pp = Preprocessor(cfg)
score, spike_index_clear, spike_index_collision = pp.process()
mp = Mainprocessor(cfg, score, spike_index_clear, spike_index_collision)
spike_train_clear, spike_index_collision = mp.mainProcess()
dc = Deconvolution(cfg, np.transpose(mp.templates, [1, 0, 2]),
spike_index_collision)
spike_train = dc.fullMPMU()
spike_train