def test_cluster(path_to_threshold_config):
yass.set_config(path_to_threshold_config)
(standarized_path, standarized_params, whiten_filter) = preprocess.run()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path, standarized_params,
whiten_filter)
cluster.run(score, spike_index_clear)
clean_tmp()
def test_cluster_nnet(path_to_config, make_tmp_folder):
yass.set_config(path_to_config, make_tmp_folder)
(standarized_path,
standarized_params,
whiten_filter) = preprocess.run()
spike_index_all = detect.run(standarized_path,
standarized_params,
whiten_filter)
cluster.run(None, spike_index_all)
def test_templates_returns_expected_results(path_to_config,
path_to_output_reference,
make_tmp_folder):
yass.set_config(path_to_config, make_tmp_folder)
(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,
save_results=True)
path_to_templates = path.join(path_to_output_reference,
'templates.npy')
ReferenceTesting.assert_array_equal(templates_, path_to_templates)
def test_templates_loads_from_disk_if_files_exist(caplog,
path_to_threshold_config):
yass.set_config(path_to_threshold_config)
(standarized_path, standarized_params, channel_index,
whiten_filter) = preprocess.run()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path,
standarized_params,
channel_index,
whiten_filter)
spike_train_clear, tmp_loc, vbParam = cluster.run(
score, spike_index_clear)
# save results
templates.run(spike_train_clear, tmp_loc, save_results=True)
assert templates.run.executed
# next time this should not run and just load from files
templates.run(spike_train_clear, tmp_loc, save_results=True)
assert not templates.run.executed
def test_templates_returns_expected_results(path_to_threshold_config,
path_to_data_folder):
np.random.seed(0)
yass.set_config(path_to_threshold_config)
(standarized_path, standarized_params, channel_index,
whiten_filter) = preprocess.run()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path,
standarized_params,
channel_index,
whiten_filter)
spike_train_clear, tmp_loc, vbParam = cluster.run(score, spike_index_clear)
(templates_, spike_train,
groups, idx_good_templates) = templates.run(spike_train_clear, tmp_loc)
path_to_templates = path.join(path_to_data_folder,
'output_reference',
'templates.npy')
ReferenceTesting.assert_array_equal(templates_, path_to_templates)
clean_tmp()
def initial_block(TMP_FOLDER, standardized_path, standardized_params,
run_chunk_sec):
logger = logging.getLogger(__name__)
if not os.path.exists(TMP_FOLDER):
os.makedirs(TMP_FOLDER)
''' **********************************************
************** DETECT EVENTS *****************
**********************************************
'''
# detect
logger.info('INITIAL DETECTION')
spike_index_path = detect.run(standardized_path,
standardized_params,
os.path.join(TMP_FOLDER, 'detect'),
run_chunk_sec=run_chunk_sec)
logger.info('INITIAL CLUSTERING')
# cluster
raw_data = True
full_run = True
fname_templates, fname_spike_train = cluster.run(
spike_index_path, standardized_path, standardized_params['dtype'],
os.path.join(TMP_FOLDER, 'cluster'), raw_data, full_run)
methods = ['duplicate', 'high_mad', 'collision']
fname_templates, fname_spike_train = postprocess.run(
methods, fname_templates, fname_spike_train,
os.path.join(TMP_FOLDER, 'cluster_post_process'), standardized_path,
standardized_params['dtype'])
return fname_templates, fname_spike_train
def test_cluster_nnet(path_to_config, make_tmp_folder):
yass.set_config(path_to_config, make_tmp_folder)
(standardized_path,
standardized_params) = preprocess.run(
os.path.join(make_tmp_folder, 'preprocess'))
spike_index_path = detect.run(
standardized_path, standardized_params,
os.path.join(make_tmp_folder, 'detect'))
cluster.run(
spike_index_path,
standardized_path,
standardized_params['dtype'],
os.path.join(make_tmp_folder, 'cluster'),
True,
True)
def test_deconvolution(patch_triage_network, path_to_config, make_tmp_folder):
yass.set_config(path_to_config, make_tmp_folder)
(standarized_path, standarized_params, whiten_filter) = preprocess.run()
spike_index_all = detect.run(standarized_path, standarized_params,
whiten_filter)
cluster.run(None, spike_index_all)
CONFIG = read_config()
TMP_FOLDER = CONFIG.path_to_output_directory
path_to_spike_train_cluster = path.join(TMP_FOLDER,
'spike_train_cluster.npy')
spike_train_cluster = np.load(path_to_spike_train_cluster)
templates_cluster = np.load(path.join(TMP_FOLDER, 'templates_cluster.npy'))
spike_train, postdeconv_templates = deconvolve.run(spike_train_cluster,
templates_cluster)
def test_cluster_loads_from_disk_if_all_files_exist(caplog,
path_to_threshold_config):
yass.set_config(path_to_threshold_config)
(standarized_path, standarized_params, whiten_filter) = preprocess.run()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path, standarized_params,
whiten_filter)
# save results
cluster.run(score, spike_index_clear, save_results=True)
assert cluster.run.executed
# next time this should not run and just load from files
cluster.run(score, spike_index_clear, save_results=True)
assert not cluster.run.executed
def main():
settings.run()
start = datetime.now()
logger = logging.getLogger(__name__)
logger.info('Preprocessing started at second: %.2f',
(datetime.now() - start).total_seconds())
# preprocessing
(standarized_path, standarized_params, channel_index,
whiten_filter) = preprocess.run(output_directory='profiling',
if_file_exists='overwrite')
logger.info('Preprocessing finished and detection started at second: %.2f',
(datetime.now() - start).total_seconds())
# detection
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path,
standarized_params,
channel_index,
whiten_filter,
output_directory='profiling',
if_file_exists='overwrite',
save_results=True)
logger.info('Detection finished and clustering started at second: %.2f',
(datetime.now() - start).total_seconds())
# clustering
spike_train_clear = cluster.run(score, spike_index_clear,
output_directory='profiling',
if_file_exists='overwrite',
save_results=True)
logger.info('Clustering finished and templates started at second: %.2f',
(datetime.now() - start).total_seconds())
# templates
the_templates = templates.run(spike_train_clear,
output_directory='profiling',
if_file_exists='overwrite',
save_results=True)
logger.info('templates finished and deconvolution started at second: %.2f',
(datetime.now() - start).total_seconds())
# deconvolution
deconvolute.run(spike_index_all, the_templates,
output_directory='profiling')
logger.info('Deconvolution finished at second: %.2f',
(datetime.now() - start).total_seconds())
def test_decovnolution(path_to_threshold_config):
yass.set_config('tests/config_nnet.yaml')
(standarized_path, standarized_params, whiten_filter) = preprocess.run()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path, standarized_params,
whiten_filter)
spike_train_clear, tmp_loc, vbParam = cluster.run(score, spike_index_clear)
(templates_, spike_train, groups,
idx_good_templates) = templates.run(spike_train_clear, tmp_loc)
deconvolute.run(spike_index_all, templates_)
clean_tmp()
def test_deconvolution(patch_triage_network, path_to_config, make_tmp_folder):
yass.set_config(path_to_config, make_tmp_folder)
(standardized_path, standardized_params) = preprocess.run(
os.path.join(make_tmp_folder, 'preprocess'))
spike_index_path = detect.run(standardized_path, standardized_params,
os.path.join(make_tmp_folder, 'detect'))
fname_templates, fname_spike_train = cluster.run(
spike_index_path, standardized_path, standardized_params['dtype'],
os.path.join(make_tmp_folder, 'cluster'), True, True)
(fname_templates, fname_spike_train, fname_templates_up,
fname_spike_train_up) = deconvolve.run(
fname_templates, os.path.join(make_tmp_folder, 'deconv'),
standardized_path, standardized_params['dtype'])
def test_templates(path_to_threshold_config):
yass.set_config(path_to_threshold_config)
(standarized_path, standarized_params, channel_index,
whiten_filter) = preprocess.run()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path,
standarized_params,
channel_index,
whiten_filter)
spike_train_clear, tmp_loc, vbParam = cluster.run(
score, spike_index_clear)
templates.run(spike_train_clear, tmp_loc)
clean_tmp()
def main():
settings.run()
start = datetime.now()
logger = logging.getLogger(__name__)
logger.info('Preprocessing started at second: %.2f',
(datetime.now() - start).total_seconds())
# preprocessing
(standarized_path, standarized_params, channel_index,
whiten_filter) = preprocess.run()
logger.info('Preprocessing finished and detection started at second: %.2f',
(datetime.now() - start).total_seconds())
# detection
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path, standarized_params,
channel_index, whiten_filter)
logger.info('Detection finished and clustering started at second: %.2f',
(datetime.now() - start).total_seconds())
# clustering
spike_train_clear = cluster.run(score, spike_index_clear)
logger.info('Clustering finished and templates started at second: %.2f',
(datetime.now() - start).total_seconds())
# templates
the_templates = templates.run(spike_train_clear)
logger.info('templates finished and deconvolution started at second: %.2f',
(datetime.now() - start).total_seconds())
# deconvolution
deconvolute.run(spike_index_all, the_templates)
logger.info('Deconvolution finished at second: %.2f',
(datetime.now() - start).total_seconds())
def test_cluster_returns_expected_results(path_to_threshold_config,
path_to_data_folder):
np.random.seed(0)
yass.set_config(path_to_threshold_config)
(standarized_path, standarized_params, whiten_filter) = preprocess.run()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path, standarized_params,
whiten_filter)
spike_train, tmp_loc, vbParam = cluster.run(score, spike_index_clear)
path_to_spike_train = path.join(path_to_data_folder, 'output_reference',
'cluster_spike_train.npy')
path_to_tmp_loc = path.join(path_to_data_folder, 'output_reference',
'cluster_tmp_loc.npy')
ReferenceTesting.assert_array_equal(spike_train, path_to_spike_train)
ReferenceTesting.assert_array_equal(tmp_loc, path_to_tmp_loc)
clean_tmp()
def run(config, logger_level='INFO', clean=False, output_dir='tmp/'):
"""Run YASS built-in pipeline
Parameters
----------
config: str or mapping (such as dictionary)
Path to YASS configuration file or mapping object
logger_level: str
Logger level
clean: bool, optional
Delete CONFIG.data.root_folder/output_dir/ before running
output_dir: str, optional
Output directory (if relative, it makes it relative to
CONFIG.data.root_folder) to store the output data, defaults to tmp/.
If absolute, it leaves it as it is.
complete: bool, optional
Generates extra files (needed to generate phy files)
Notes
-----
Running the preprocessor will generate the followiing files in
CONFIG.data.root_folder/output_directory/:
* ``config.yaml`` - Copy of the configuration file
* ``metadata.yaml`` - Experiment metadata
* ``filtered.bin`` - Filtered recordings (from preprocess)
* ``filtered.yaml`` - Filtered recordings metadata (from preprocess)
* ``standardized.bin`` - Standarized recordings (from preprocess)
* ``standardized.yaml`` - Standarized recordings metadata (from preprocess)
* ``whitening.npy`` - Whitening filter (from preprocess)
Returns
-------
numpy.ndarray
Spike train
"""
# load yass configuration parameters
CONFIG = Config.from_yaml(config)
CONFIG._data['cluster']['min_fr'] = 1
CONFIG._data['clean_up']['mad']['min_var_gap'] = 1.5
CONFIG._data['clean_up']['mad']['max_violations'] = 5
CONFIG._data['neuralnetwork']['apply_nn'] = False
CONFIG._data['detect']['threshold'] = 4
set_config(CONFIG._data, output_dir)
CONFIG = read_config()
TMP_FOLDER = CONFIG.path_to_output_directory
# remove tmp folder if needed
if os.path.exists(TMP_FOLDER) and clean:
shutil.rmtree(TMP_FOLDER)
# create TMP_FOLDER if needed
if not os.path.exists(TMP_FOLDER):
os.makedirs(TMP_FOLDER)
# load logging config file
logging_config = load_logging_config_file()
logging_config['handlers']['file']['filename'] = os.path.join(
TMP_FOLDER, 'yass.log')
logging_config['root']['level'] = logger_level
# configure logging
logging.config.dictConfig(logging_config)
# instantiate logger
logger = logging.getLogger(__name__)
# print yass version
logger.info('YASS version: %s', yass.__version__)
''' **********************************************
******** SET ENVIRONMENT VARIABLES ***********
**********************************************
'''
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["GIO_EXTRA_MODULES"] = "/usr/lib/x86_64-linux-gnu/gio/modules/"
# TODO: if input spike train is None, run yass with threshold detector
#if fname_spike_train is None:
# logger.info('Not available yet. You must input spike train')
# return
''' **********************************************
************** PREPROCESS ********************
**********************************************
'''
# preprocess
start = time.time()
(standardized_path, standardized_dtype) = preprocess.run(
os.path.join(TMP_FOLDER, 'preprocess'))
TMP_FOLDER = os.path.join(TMP_FOLDER, 'nn_train')
if not os.path.exists(TMP_FOLDER):
os.makedirs(TMP_FOLDER)
if CONFIG.neuralnetwork.training.input_spike_train_filname is None:
# run on 10 minutes of data
rec_len = np.min(
(CONFIG.rec_len, CONFIG.recordings.sampling_rate * 10 * 60))
# detect
logger.info('DETECTION')
spike_index_path = detect.run(standardized_path,
standardized_dtype,
os.path.join(TMP_FOLDER, 'detect'),
run_chunk_sec=[0, rec_len])
logger.info('CLUSTERING')
# cluster
raw_data = True
full_run = False
fname_templates, fname_spike_train = cluster.run(
os.path.join(TMP_FOLDER, 'cluster'),
standardized_path,
standardized_dtype,
fname_spike_index=spike_index_path,
raw_data=True,
full_run=True)
methods = [
'off_center', 'low_ptp', 'high_mad', 'duplicate', 'duplicate_l2'
]
fname_templates, fname_spike_train = postprocess.run(
methods, os.path.join(TMP_FOLDER,
'cluster_post_process'), standardized_path,
standardized_dtype, fname_templates, fname_spike_train)
else:
# if there is an input spike train, use it
fname_spike_train = CONFIG.neuralnetwork.training.input_spike_train_filname
# Get training data maker
DetectTD, DenoTD = augment.run(standardized_path, standardized_dtype,
fname_spike_train,
os.path.join(TMP_FOLDER, 'augment'))
# Train Detector
detector = Detect(CONFIG.neuralnetwork.detect.n_filters,
CONFIG.spike_size_nn, CONFIG.channel_index).cuda()
detector.train(os.path.join(TMP_FOLDER, 'detect.pt'), DetectTD)
# Train Denoiser
denoiser = Denoise(CONFIG.neuralnetwork.denoise.n_filters,
CONFIG.neuralnetwork.denoise.filter_sizes,
CONFIG.spike_size_nn).cuda()
denoiser.train(os.path.join(TMP_FOLDER, 'denoise.pt'), DenoTD)
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)
def run(config,
logger_level='INFO',
clean=False,
output_dir='tmp/',
complete=False,
set_zero_seed=False):
"""Run YASS built-in pipeline
Parameters
----------
config: str or mapping (such as dictionary)
Path to YASS configuration file or mapping object
logger_level: str
Logger level
clean: bool, optional
Delete CONFIG.data.root_folder/output_dir/ before running
output_dir: str, optional
Output directory (if relative, it makes it relative to
CONFIG.data.root_folder) to store the output data, defaults to tmp/.
If absolute, it leaves it as it is.
complete: bool, optional
Generates extra files (needed to generate phy files)
Notes
-----
Running the preprocessor will generate the followiing files in
CONFIG.data.root_folder/output_directory/:
* ``config.yaml`` - Copy of the configuration file
* ``metadata.yaml`` - Experiment metadata
* ``filtered.bin`` - Filtered recordings (from preprocess)
* ``filtered.yaml`` - Filtered recordings metadata (from preprocess)
* ``standardized.bin`` - Standarized recordings (from preprocess)
* ``standardized.yaml`` - Standarized recordings metadata (from preprocess)
* ``whitening.npy`` - Whitening filter (from preprocess)
Returns
-------
numpy.ndarray
Spike train
"""
# load yass configuration parameters
set_config(config, output_dir)
CONFIG = read_config()
TMP_FOLDER = CONFIG.path_to_output_directory
# remove tmp folder if needed
if os.path.exists(TMP_FOLDER) and clean:
shutil.rmtree(TMP_FOLDER)
# create TMP_FOLDER if needed
if not os.path.exists(TMP_FOLDER):
os.makedirs(TMP_FOLDER)
# load logging config file
logging_config = load_logging_config_file()
logging_config['handlers']['file']['filename'] = path.join(
TMP_FOLDER, 'yass.log')
logging_config['root']['level'] = logger_level
# configure logging
logging.config.dictConfig(logging_config)
# instantiate logger
logger = logging.getLogger(__name__)
# print yass version
logger.info('YASS version: %s', yass.__version__)
''' **********************************************
******** SET ENVIRONMENT VARIABLES ***********
**********************************************
'''
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["GIO_EXTRA_MODULES"] = "/usr/lib/x86_64-linux-gnu/gio/modules/"
''' **********************************************
************** PREPROCESS ********************
**********************************************
'''
# preprocess
start = time.time()
(standardized_path, standardized_params, whiten_filter) = (preprocess.run(
if_file_exists=CONFIG.preprocess.if_file_exists))
time_preprocess = time.time() - start
''' **********************************************
************** DETECT EVENTS *****************
**********************************************
'''
# detect
# Cat: This code now runs with open tensorflow calls
start = time.time()
(spike_index_all) = detect.run(standardized_path,
standardized_params,
whiten_filter,
if_file_exists=CONFIG.detect.if_file_exists,
save_results=CONFIG.detect.save_results)
spike_index_clear = None
time_detect = time.time() - start
''' **********************************************
***************** CLUSTER ********************
**********************************************
'''
# cluster
start = time.time()
path_to_spike_train_cluster = path.join(TMP_FOLDER,
'spike_train_cluster.npy')
if os.path.exists(path_to_spike_train_cluster) == False:
cluster.run(spike_index_clear, spike_index_all)
else:
print("\nClustering completed previously...\n\n")
spike_train_cluster = np.load(path_to_spike_train_cluster)
templates_cluster = np.load(
os.path.join(TMP_FOLDER, 'templates_cluster.npy'))
time_cluster = time.time() - start
#print ("Spike train clustered: ", spike_index_cluster.shape, "spike train clear: ",
# spike_train_clear.shape, " templates: ", templates.shape)
''' **********************************************
************** DECONVOLUTION *****************
**********************************************
'''
# run deconvolution
start = time.time()
spike_train, postdeconv_templates = deconvolve.run(spike_train_cluster,
templates_cluster)
time_deconvolution = time.time() - start
# save spike train
path_to_spike_train = path.join(TMP_FOLDER,
'spike_train_post_deconv_post_merge.npy')
np.save(path_to_spike_train, spike_train)
logger.info('Spike train saved in: {}'.format(path_to_spike_train))
# save template
path_to_templates = path.join(TMP_FOLDER,
'templates_post_deconv_post_merge.npy')
np.save(path_to_templates, postdeconv_templates)
logger.info('Templates saved in: {}'.format(path_to_templates))
''' **********************************************
************** POST PROCESSING****************
**********************************************
'''
# save metadata in tmp
path_to_metadata = path.join(TMP_FOLDER, 'metadata.yaml')
logging.info('Saving metadata in {}'.format(path_to_metadata))
save_metadata(path_to_metadata)
# save metadata in tmp
path_to_metadata = path.join(TMP_FOLDER, 'metadata.yaml')
logging.info('Saving metadata in {}'.format(path_to_metadata))
save_metadata(path_to_metadata)
# save config.yaml copy in tmp/
path_to_config_copy = path.join(TMP_FOLDER, 'config.yaml')
if isinstance(config, Mapping):
with open(path_to_config_copy, 'w') as f:
yaml.dump(config, f, default_flow_style=False)
else:
shutil.copy2(config, path_to_config_copy)
logging.info('Saving copy of config: {} in {}'.format(
config, path_to_config_copy))
# this part loads waveforms for all spikes in the spike train and scores
# them, this data is needed to later generate phy files
if complete:
STANDARIZED_PATH = path.join(TMP_FOLDER, 'standardized.bin')
PARAMS = load_yaml(path.join(TMP_FOLDER, 'standardized.yaml'))
# load waveforms for all spikes in the spike train
logger.info('Loading waveforms from all spikes in the spike train...')
explorer = RecordingExplorer(STANDARIZED_PATH,
spike_size=CONFIG.spike_size,
dtype=PARAMS['dtype'],
n_channels=PARAMS['n_channels'],
data_order=PARAMS['data_order'])
waveforms = explorer.read_waveforms(spike_train[:, 0])
path_to_waveforms = path.join(TMP_FOLDER, 'spike_train_waveforms.npy')
np.save(path_to_waveforms, waveforms)
logger.info('Saved all waveforms from the spike train in {}...'.format(
path_to_waveforms))
# score all waveforms
logger.info('Scoring waveforms from all spikes in the spike train...')
path_to_rotation = path.join(TMP_FOLDER, 'rotation.npy')
rotation = np.load(path_to_rotation)
main_channels = explorer.main_channel_for_waveforms(waveforms)
path_to_main_channels = path.join(TMP_FOLDER,
'waveforms_main_channel.npy')
np.save(path_to_main_channels, main_channels)
logger.info('Saved all waveforms main channels in {}...'.format(
path_to_waveforms))
waveforms_score = dim_red.score(waveforms, rotation, main_channels,
CONFIG.neigh_channels, CONFIG.geom)
path_to_waveforms_score = path.join(TMP_FOLDER, 'waveforms_score.npy')
np.save(path_to_waveforms_score, waveforms_score)
logger.info('Saved all scores in {}...'.format(path_to_waveforms))
# score templates
# TODO: templates should be returned in the right shape to avoid .T
templates_ = templates.T
main_channels_tmpls = explorer.main_channel_for_waveforms(templates_)
path_to_templates_main_c = path.join(TMP_FOLDER,
'templates_main_channel.npy')
np.save(path_to_templates_main_c, main_channels_tmpls)
logger.info('Saved all templates main channels in {}...'.format(
path_to_templates_main_c))
templates_score = dim_red.score(templates_, rotation,
main_channels_tmpls,
CONFIG.neigh_channels, CONFIG.geom)
path_to_templates_score = path.join(TMP_FOLDER, 'templates_score.npy')
np.save(path_to_templates_score, templates_score)
logger.info(
'Saved all templates scores in {}...'.format(path_to_waveforms))
logger.info('Finished YASS execution. Timing summary:')
total = (time_preprocess + time_detect + time_cluster + time_deconvolution)
logger.info('\t Preprocess: %s (%.2f %%)',
human_readable_time(time_preprocess),
time_preprocess / total * 100)
logger.info('\t Detection: %s (%.2f %%)', human_readable_time(time_detect),
time_detect / total * 100)
logger.info('\t Clustering: %s (%.2f %%)',
human_readable_time(time_cluster), time_cluster / total * 100)
logger.info('\t Deconvolution: %s (%.2f %%)',
human_readable_time(time_deconvolution),
time_deconvolution / total * 100)
return spike_train
def test_new_process_shows_error_if_empty_config():
with pytest.raises(ValueError):
cluster.run(None, None)
def iterative_block(TMP_FOLDER,
standardized_path,
standardized_params,
fname_templates,
run_chunk_sec):
logger = logging.getLogger(__name__)
if not os.path.exists(TMP_FOLDER):
os.makedirs(TMP_FOLDER)
# run deconvolution
logger.info('DECONV')
(fname_templates,
fname_spike_train,
fname_templates_up,
fname_spike_train_up,
fname_shifts) = deconvolve.run(
fname_templates,
os.path.join(TMP_FOLDER,
'deconv'),
standardized_path,
standardized_params['dtype'],
run_chunk_sec=run_chunk_sec)
# compute residual
logger.info('RESIDUAL COMPUTATION')
fname_residual, residual_dtype = residual.run(
fname_shifts,
fname_templates_up,
fname_spike_train_up,
os.path.join(TMP_FOLDER,
'residual'),
standardized_path,
standardized_params['dtype'],
dtype_out='float32',
run_chunk_sec=run_chunk_sec)
logger.info('BLOCK1 MERGE')
fname_templates_up, fname_spike_train_up = merge.run(
os.path.join(TMP_FOLDER,
'post_deconv_merge'),
False,
fname_spike_train,
fname_templates,
fname_spike_train_up,
fname_templates_up,
standardized_path,
standardized_params['dtype'],
fname_residual,
residual_dtype)
fname_templates = fname_templates_up
fname_spike_train = fname_spike_train_up
# cluster
logger.info('RECLUSTERING')
raw_data = False
full_run = True
fname_templates, fname_spike_train = cluster.run(
fname_spike_train,
standardized_path,
standardized_params['dtype'],
os.path.join(TMP_FOLDER, 'cluster'),
raw_data,
full_run,
fname_residual=fname_residual,
residual_dtype=residual_dtype,
fname_templates_up=fname_templates_up,
fname_spike_train_up=fname_spike_train_up)
methods = ['duplicate', 'high_mad', 'collision']
fname_templates, fname_spike_train = postprocess.run(
methods,
fname_templates,
fname_spike_train,
os.path.join(TMP_FOLDER,
'cluster_post_process'),
standardized_path,
standardized_params['dtype'])
return fname_templates, fname_spike_train
def test_nn_output(path_to_tests):
"""Test that pipeline using threshold detector returns the same results
"""
logger = logging.getLogger(__name__)
yass.set_config(path.join(path_to_tests, 'config_nn_49.yaml'))
CONFIG = read_config()
TMP = Path(CONFIG.data.root_folder, 'tmp')
logger.info('Removing %s', TMP)
shutil.rmtree(str(TMP))
PATH_TO_REF = '/home/Edu/data/nnet'
np.random.seed(0)
# run preprocess
(standarized_path, standarized_params, whiten_filter) = preprocess.run()
# load preprocess output
path_to_standarized = path.join(PATH_TO_REF, 'preprocess',
'standarized.bin')
path_to_whitening = path.join(PATH_TO_REF, 'preprocess', 'whitening.npy')
whitening_saved = np.load(path_to_whitening)
standarized_saved = RecordingsReader(path_to_standarized,
loader='array').data
standarized = RecordingsReader(standarized_path, loader='array').data
# test preprocess
np.testing.assert_array_equal(whitening_saved, whiten_filter)
np.testing.assert_array_equal(standarized_saved, standarized)
# run detect
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path, standarized_params,
whiten_filter)
# load detect output
path_to_scores = path.join(PATH_TO_REF, 'detect', 'scores_clear.npy')
path_to_spike_index_clear = path.join(PATH_TO_REF, 'detect',
'spike_index_clear.npy')
path_to_spike_index_all = path.join(PATH_TO_REF, 'detect',
'spike_index_all.npy')
scores_saved = np.load(path_to_scores)
spike_index_clear_saved = np.load(path_to_spike_index_clear)
spike_index_all_saved = np.load(path_to_spike_index_all)
# test detect output
np.testing.assert_array_equal(scores_saved, score)
np.testing.assert_array_equal(spike_index_clear_saved, spike_index_clear)
np.testing.assert_array_equal(spike_index_all_saved, spike_index_all)
# run cluster
(spike_train_clear, tmp_loc,
vbParam) = cluster.run(score, spike_index_clear)
# load cluster output
path_to_spike_train_cluster = path.join(PATH_TO_REF, 'cluster',
'spike_train_cluster.npy')
spike_train_cluster_saved = np.load(path_to_spike_train_cluster)
# test cluster
#np.testing.assert_array_equal(spike_train_cluster_saved, spike_train_clear)
# run templates
(templates_, spike_train, groups,
idx_good_templates) = templates.run(spike_train_clear,
tmp_loc,
save_results=True)
# load templates output
path_to_templates = path.join(PATH_TO_REF, 'templates', 'templates.npy')
templates_saved = np.load(path_to_templates)
# test templates
np.testing.assert_array_almost_equal(templates_saved,
templates_,
decimal=4)
# run deconvolution
spike_train = deconvolute.run(spike_index_all, templates_)
# load deconvolution output
path_to_spike_train = path.join(PATH_TO_REF, 'spike_train.npy')
spike_train_saved = np.load(path_to_spike_train)
# test deconvolution
np.testing.assert_array_equal(spike_train_saved, spike_train)
def iterative_block(TMP_FOLDER, standardized_path, standardized_dtype,
fname_templates, run_chunk_sec):
logger = logging.getLogger(__name__)
if not os.path.exists(TMP_FOLDER):
os.makedirs(TMP_FOLDER)
# run deconvolution
logger.info('DECONV')
(fname_templates, fname_spike_train, fname_templates_up,
fname_spike_train_up,
fname_shifts) = deconvolve.run(fname_templates,
os.path.join(TMP_FOLDER, 'deconv'),
standardized_path,
standardized_dtype,
run_chunk_sec=run_chunk_sec)
# compute residual
logger.info('RESIDUAL COMPUTATION')
fname_residual, residual_dtype = residual.run(fname_shifts,
fname_templates,
fname_spike_train,
os.path.join(
TMP_FOLDER, 'residual'),
standardized_path,
standardized_dtype,
dtype_out='float32',
run_chunk_sec=run_chunk_sec)
#logger.info('KILL NOISE')
#fname_spike_train2 = noise.run(
# fname_templates,
# fname_spike_train,
# fname_shifts,
# os.path.join(TMP_FOLDER,
# 'noise_kill'),
# fname_residual,
# residual_dtype)
if False:
logger.info('SOFT NOISE ASSIGNMENT')
fname_soft_assignment = noise.run(
fname_templates, fname_spike_train, fname_shifts,
os.path.join(TMP_FOLDER, 'soft_assignment'), fname_residual,
residual_dtype)
logger.info('BLOCK1 MERGE')
_, _, _ = merge.run(os.path.join(TMP_FOLDER, 'post_deconv_merge'),
fname_spike_train, fname_templates,
fname_soft_assignment, fname_residual,
residual_dtype)
# cluster
logger.info('RECLUSTERING')
fname_templates, fname_spike_train = cluster.run(
os.path.join(TMP_FOLDER, 'cluster'),
standardized_path,
standardized_dtype,
fname_residual=fname_residual,
residual_dtype=residual_dtype,
fname_spike_index=None,
fname_templates=fname_templates,
fname_spike_train=fname_spike_train,
raw_data=False,
full_run=True)
methods = ['off_center', 'high_mad', 'duplicate_l2', 'duplicate']
fname_templates, fname_spike_train = postprocess.run(
methods, fname_templates, fname_spike_train,
os.path.join(TMP_FOLDER, 'cluster_post_process'), standardized_path,
standardized_dtype)
return fname_templates, fname_spike_train
def run(config, logger_level='INFO', clean=False, output_dir='tmp/',
complete=False, set_zero_seed=False):
"""Run YASS built-in pipeline
Parameters
----------
config: str or mapping (such as dictionary)
Path to YASS configuration file or mapping object
logger_level: str
Logger level
clean: bool, optional
Delete CONFIG.data.root_folder/output_dir/ before running
output_dir: str, optional
Output directory (relative to CONFIG.data.root_folder to store the
output data, defaults to tmp/
complete: bool, optional
Generates extra files (needed to generate phy files)
Notes
-----
Running the preprocessor will generate the followiing files in
CONFIG.data.root_folder/output_directory/:
* ``config.yaml`` - Copy of the configuration file
* ``metadata.yaml`` - Experiment metadata
* ``filtered.bin`` - Filtered recordings (from preprocess)
* ``filtered.yaml`` - Filtered recordings metadata (from preprocess)
* ``standarized.bin`` - Standarized recordings (from preprocess)
* ``standarized.yaml`` - Standarized recordings metadata (from preprocess)
* ``whitening.npy`` - Whitening filter (from preprocess)
Returns
-------
numpy.ndarray
Spike train
"""
# load yass configuration parameters
set_config(config)
CONFIG = read_config()
ROOT_FOLDER = CONFIG.data.root_folder
TMP_FOLDER = path.join(ROOT_FOLDER, output_dir)
# remove tmp folder if needed
if os.path.exists(TMP_FOLDER) and clean:
shutil.rmtree(TMP_FOLDER)
# create TMP_FOLDER if needed
if not os.path.exists(TMP_FOLDER):
os.makedirs(TMP_FOLDER)
# load logging config file
logging_config = load_logging_config_file()
logging_config['handlers']['file']['filename'] = path.join(TMP_FOLDER,
'yass.log')
logging_config['root']['level'] = logger_level
# configure logging
logging.config.dictConfig(logging_config)
# instantiate logger and start coloredlogs
logger = logging.getLogger(__name__)
coloredlogs.install(logger=logger)
if set_zero_seed:
logger.warning('Set numpy seed to zero')
np.random.seed(0)
# print yass version
logger.info('YASS version: %s', yass.__version__)
# preprocess
start = time.time()
(standarized_path,
standarized_params,
channel_index,
whiten_filter) = (preprocess
.run(output_directory=output_dir,
if_file_exists=CONFIG.preprocess.if_file_exists))
time_preprocess = time.time() - start
# detect
start = time.time()
(score, spike_index_clear,
spike_index_all) = detect.run(standarized_path,
standarized_params,
channel_index,
whiten_filter,
output_directory=output_dir,
if_file_exists=CONFIG.detect.if_file_exists,
save_results=CONFIG.detect.save_results)
time_detect = time.time() - start
# cluster
start = time.time()
spike_train_clear, tmp_loc, vbParam = cluster.run(
score,
spike_index_clear,
output_directory=output_dir,
if_file_exists=CONFIG.cluster.if_file_exists,
save_results=CONFIG.cluster.save_results)
time_cluster = time.time() - start
# get templates
start = time.time()
(templates,
spike_train_clear_after_templates,
groups,
idx_good_templates) = get_templates.run(
spike_train_clear, tmp_loc,
output_directory=output_dir,
if_file_exists=CONFIG.templates.if_file_exists,
save_results=CONFIG.templates.save_results)
time_templates = time.time() - start
# run deconvolution
start = time.time()
spike_train = deconvolute.run(spike_index_all, templates,
output_directory=output_dir)
time_deconvolution = time.time() - start
# save metadata in tmp
path_to_metadata = path.join(TMP_FOLDER, 'metadata.yaml')
logging.info('Saving metadata in {}'.format(path_to_metadata))
save_metadata(path_to_metadata)
# save config.yaml copy in tmp/
path_to_config_copy = path.join(TMP_FOLDER, 'config.yaml')
if isinstance(config, Mapping):
with open(path_to_config_copy, 'w') as f:
yaml.dump(config, f, default_flow_style=False)
else:
shutil.copy2(config, path_to_config_copy)
logging.info('Saving copy of config: {} in {}'.format(config,
path_to_config_copy))
# TODO: complete flag saves other files needed for integrating phy
# with yass, the integration hasn't been completed yet
# this part loads waveforms for all spikes in the spike train and scores
# them, this data is needed to later generate phy files
if complete:
STANDARIZED_PATH = path.join(TMP_FOLDER, 'standarized.bin')
PARAMS = load_yaml(path.join(TMP_FOLDER, 'standarized.yaml'))
# load waveforms for all spikes in the spike train
logger.info('Loading waveforms from all spikes in the spike train...')
explorer = RecordingExplorer(STANDARIZED_PATH,
spike_size=CONFIG.spike_size,
dtype=PARAMS['dtype'],
n_channels=PARAMS['n_channels'],
data_order=PARAMS['data_order'])
waveforms = explorer.read_waveforms(spike_train[:, 0])
path_to_waveforms = path.join(TMP_FOLDER, 'spike_train_waveforms.npy')
np.save(path_to_waveforms, waveforms)
logger.info('Saved all waveforms from the spike train in {}...'
.format(path_to_waveforms))
# score all waveforms
logger.info('Scoring waveforms from all spikes in the spike train...')
path_to_rotation = path.join(TMP_FOLDER, 'rotation.npy')
rotation = np.load(path_to_rotation)
main_channels = explorer.main_channel_for_waveforms(waveforms)
path_to_main_channels = path.join(TMP_FOLDER,
'waveforms_main_channel.npy')
np.save(path_to_main_channels, main_channels)
logger.info('Saved all waveforms main channels in {}...'
.format(path_to_waveforms))
waveforms_score = dim_red.score(waveforms, rotation, main_channels,
CONFIG.neigh_channels, CONFIG.geom)
path_to_waveforms_score = path.join(TMP_FOLDER, 'waveforms_score.npy')
np.save(path_to_waveforms_score, waveforms_score)
logger.info('Saved all scores in {}...'.format(path_to_waveforms))
# score templates
# TODO: templates should be returned in the right shape to avoid .T
templates_ = templates.T
main_channels_tmpls = explorer.main_channel_for_waveforms(templates_)
path_to_templates_main_c = path.join(TMP_FOLDER,
'templates_main_channel.npy')
np.save(path_to_templates_main_c, main_channels_tmpls)
logger.info('Saved all templates main channels in {}...'
.format(path_to_templates_main_c))
templates_score = dim_red.score(templates_, rotation,
main_channels_tmpls,
CONFIG.neigh_channels, CONFIG.geom)
path_to_templates_score = path.join(TMP_FOLDER, 'templates_score.npy')
np.save(path_to_templates_score, templates_score)
logger.info('Saved all templates scores in {}...'
.format(path_to_waveforms))
logger.info('Finished YASS execution. Timing summary:')
total = (time_preprocess + time_detect + time_cluster + time_templates
+ time_deconvolution)
logger.info('\t Preprocess: %s (%.2f %%)',
human_readable_time(time_preprocess),
time_preprocess/total*100)
logger.info('\t Detection: %s (%.2f %%)',
human_readable_time(time_detect),
time_detect/total*100)
logger.info('\t Clustering: %s (%.2f %%)',
human_readable_time(time_cluster),
time_cluster/total*100)
logger.info('\t Templates: %s (%.2f %%)',
human_readable_time(time_templates),
time_templates/total*100)
logger.info('\t Deconvolution: %s (%.2f %%)',
human_readable_time(time_deconvolution),
time_deconvolution/total*100)
return spike_train
