def _run_pipeline(config,
output_file,
logger_level='INFO',
clean=True,
output_dir='tmp/',
complete=False):
"""
Run the entire pipeline given a path to a config file
and output path
"""
# 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
logger = logging.getLogger(__name__)
# run preprocessor
(score, spike_index_clear,
spike_index_collision) = preprocess.run(output_directory=output_dir)
# run processor
(spike_train_clear, templates,
spike_index_collision) = process.run(score,
spike_index_clear,
spike_index_collision,
output_directory=output_dir)
# run deconvolution
spike_train = deconvolute.run(spike_train_clear,
templates,
spike_index_collision,
output_directory=output_dir)
# 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')
shutil.copy2(config, path_to_config_copy)
logging.info('Saving copy of config: {} in {}'.format(
config, path_to_config_copy))
# save templates
path_to_templates = path.join(TMP_FOLDER, 'templates.npy')
logging.info('Saving templates in {}'.format(path_to_templates))
np.save(path_to_templates, templates)
path_to_spike_train = path.join(TMP_FOLDER, output_file)
np.save(path_to_spike_train, spike_train)
logger.info('Spike train saved in: {}'.format(path_to_spike_train))
# 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.spikeSize,
dtype=PARAMS['dtype'],
n_channels=PARAMS['n_channels'],
data_format=PARAMS['data_format'])
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.neighChannels, 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.neighChannels, 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))
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 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