def set_config(config, output_directory):
"""Set configuration settings
Parameters
----------
config: str or mapping (such as dictionary)
Path to a yaml file or mapping object
output_directory: str of pathlib.Path
output directory for the project, this makes
Config.output_directory return
onfig.data.root_folder / output_directory, which is a common path
used through the pipeline. If the path is absolute, it is not modified
Notes
-----
This function lets the user configure global settings used
trhoughout the execution of the pipeline. The variables set here
determine the behavior of some steps. For that reason once, the values
are set, they cannot be changed, to avoid changing global configuration
changes at runtime
"""
# this variable is accesible in the preprocessa and process blocks to avoid
# having to pass the configuration over and over, to prevent global state
# issues, the config cannot be edited after loaded
global CONFIG
if isinstance(config, str):
CONFIG = Config.from_yaml(config, output_directory)
else:
CONFIG = Config(config, output_directory)
logger.debug('CONFIG set to: %s', CONFIG._data)
return CONFIG
def set_config(config):
"""Set configuration settings
Parameters
----------
config: str or mapping (such as dictionary)
Path to a yaml file or mapping object
Notes
-----
This function lets the user configure global settings used
trhoughout the execution of the pipeline. The variables set here
determine the behavior of some steps. For that reason once, the values
are set, they cannot be changed, to avoid changing global configuration
changes at runtime
"""
# this variable is accesible in the preprocessa and process blocks to avoid
# having to pass the configuration over and over, to prevent global state
# issues, the config cannot be edited after loaded
global CONFIG
if isinstance(config, str):
CONFIG = Config.from_yaml(config)
else:
CONFIG = Config(config)
logger.debug('CONFIG set to: %s', CONFIG._data)
def train(directory, config_train, logger_level):
"""
Train neural networks, DIRECTORY must be a folder containing the
output of `yass sort`, CONFIG_TRAIN must be the location of a file with
the training parameters
"""
logging.basicConfig(level=getattr(logging, logger_level))
logger = logging.getLogger(__name__)
path_to_spike_train = path.join(directory, 'spike_train.npy')
spike_train = np.load(path_to_spike_train)
logger.info(
'Loaded spike train with: {:,} spikes and {:,} different IDs'.format(
len(spike_train), len(np.unique(spike_train[:, 1]))))
path_to_config = path.join(directory, 'config.yaml')
CONFIG = Config.from_yaml(path_to_config)
CONFIG_TRAIN = load_yaml(config_train)
train_neural_networks(CONFIG,
CONFIG_TRAIN,
spike_train,
data_folder=directory)
def set_config(path_to_config):
"""Set configuration settings
This function lets the user configure global settings used
trhoughout the execution of the pipeline. The variables set here
determine the behavior of some steps. For that reason once, the values
are set, they cannot be changed, to avoid changing global configuration
changes at runtime
"""
# this variable is accesible in the preprocessa and process blocks to avoid
# having to pass the configuration over and over, to prevent global state
# issues, the config cannot be edited after loaded
global CONFIG
CONFIG = Config.from_yaml(path_to_config)
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)