def train(CONFIG, CONFIG_TRAIN, spike_train, data_folder):
"""
Train neural network
Parameters
----------
CONFIG
YASS configuration file
CONFIG_TRAIN
YASS Neural Network configuration file
spike_train: numpy.ndarray
Spike train, first column is spike index and second is main channel
"""
logger = logging.getLogger(__name__)
chosen_templates = CONFIG_TRAIN['templates']['ids']
min_amp = CONFIG_TRAIN['templates']['minimum_amplitude']
nspikes = CONFIG_TRAIN['training']['n_spikes']
n_filters_detect = CONFIG_TRAIN['network_detector']['n_filters']
n_iter = CONFIG_TRAIN['training']['n_iterations']
n_batch = CONFIG_TRAIN['training']['n_batch']
l2_reg_scale = CONFIG_TRAIN['training']['l2_regularization_scale']
train_step_size = CONFIG_TRAIN['training']['step_size']
detectnet_name = './' + CONFIG_TRAIN['network_detector']['name'] + '.ckpt'
n_filters_triage = CONFIG_TRAIN['network_triage']['n_filters']
triagenet_name = './' + CONFIG_TRAIN['network_triage']['name'] + '.ckpt'
n_features = CONFIG_TRAIN['network_autoencoder']['n_features']
ae_name = './' + CONFIG_TRAIN['network_autoencoder']['name'] + '.ckpt'
# generate training data for detection, triage and autoencoder
logger.info('Generating training data...')
(x_detect, y_detect, x_triage, y_triage, x_ae,
y_ae) = make_training_data(CONFIG,
spike_train,
chosen_templates,
min_amp,
nspikes,
data_folder=data_folder)
# train detector
NeuralNetDetector.train(x_detect, y_detect, n_filters_detect, n_iter,
n_batch, l2_reg_scale, train_step_size,
detectnet_name)
# train triage
NeuralNetTriage.train(x_triage, y_triage, n_filters_triage, n_iter,
n_batch, l2_reg_scale, train_step_size,
triagenet_name)
# train autoencoder
AutoEncoder.train(x_ae, y_ae, n_features, n_iter, n_batch, train_step_size,
ae_name)
def test_can_use_neural_network_detector(path_to_tests,
path_to_standarized_data):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
data = RecordingsReader(path_to_standarized_data, loader='array').data
channel_index = make_channel_index(CONFIG.neigh_channels, CONFIG.geom)
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
# instantiate neural networks
NND = NeuralNetDetector.load(detection_fname, detection_th, channel_index)
NNT = NeuralNetTriage.load(triage_fname,
triage_th,
input_tensor=NND.waveform_tf)
NNAE = AutoEncoder(ae_fname, input_tensor=NND.waveform_tf)
output_tf = (NNAE.score_tf, NND.spike_index_tf, NNT.idx_clean)
with tf.Session() as sess:
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
neuralnetwork.run_detect_triage_featurize(data, sess, NND.x_tf,
output_tf, neighbors, rot)
def test_can_use_detector_and_triage_after_fit(path_to_tests,
path_to_sample_pipeline_folder,
tmp_folder,
path_to_standarized_data):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
(x_detect, y_detect, x_triage, y_triage, x_ae,
y_ae) = make_training_data(CONFIG, spike_train, chosen_templates,
min_amplitude, n_spikes,
path_to_sample_pipeline_folder)
_, waveform_length, n_neighbors = x_detect.shape
path_to_model = path.join(tmp_folder, 'detect-net.ckpt')
detector = NeuralNetDetector(path_to_model,
filters,
waveform_length,
n_neighbors,
threshold=0.5,
channel_index=CONFIG.channel_index,
n_iter=10)
detector.fit(x_detect, y_detect)
triage = NeuralNetTriage(path_to_model,
filters,
waveform_length,
n_neighbors,
threshold=0.5,
n_iter=10)
triage.fit(x_detect, y_detect)
data = RecordingExplorer(path_to_standarized_data).reader.data
output_names = ('spike_index', 'waveform', 'probability')
(spike_index, waveform,
proba) = detector.predict(data, output_names=output_names)
triage.predict(waveform[:, :, :n_neighbors])
def get_o_layer(standarized_path,
standarized_params,
output_directory='tmp/',
output_dtype='float32',
output_filename='o_layer.bin',
if_file_exists='skip',
save_partial_results=False):
"""Get the output of NN detector instead of outputting the spike index
"""
CONFIG = read_config()
channel_index = make_channel_index(CONFIG.neigh_channels, CONFIG.geom, 1)
x_tf = tf.placeholder("float", [None, None])
# load Neural Net's
detection_fname = CONFIG.detect.neural_network_detector.filename
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
NND = NeuralNetDetector(detection_fname)
o_layer_tf = NND.make_o_layer_tf_tensors(x_tf, channel_index, detection_th)
bp = BatchProcessor(standarized_path,
standarized_params['dtype'],
standarized_params['n_channels'],
standarized_params['data_format'],
CONFIG.resources.max_memory,
buffer_size=CONFIG.spike_size)
TMP = os.path.join(CONFIG.data.root_folder, output_directory)
_output_path = os.path.join(TMP, output_filename)
(o_path, o_params) = bp.multi_channel_apply(_get_o_layer,
mode='disk',
cleanup_function=fix_indexes,
output_path=_output_path,
cast_dtype=output_dtype,
x_tf=x_tf,
o_layer_tf=o_layer_tf,
NND=NND)
return o_path, o_params
def test_can_reload_detector(path_to_tests, path_to_sample_pipeline_folder,
tmp_folder):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
(x_detect, y_detect, x_triage, y_triage, x_ae,
y_ae) = make_training_data(CONFIG, spike_train, chosen_templates,
min_amplitude, n_spikes,
path_to_sample_pipeline_folder)
_, waveform_length, n_neighbors = x_detect.shape
path_to_model = path.join(tmp_folder, 'detect-net.ckpt')
detector = NeuralNetDetector(path_to_model,
filters,
waveform_length,
n_neighbors,
threshold=0.5,
channel_index=CONFIG.channel_index,
n_iter=10)
detector.fit(x_detect, y_detect)
NeuralNetDetector.load(path_to_model,
threshold=0.5,
channel_index=CONFIG.channel_index)
def load_rotation(detector_filename, autoencoder_filename):
"""
Load neural network rotation matrix
"""
# FIXME: this function should not ask for detector_filename, it is not
# needed
nnd = NeuralNetDetector(detector_filename, autoencoder_filename)
with tf.Session() as sess:
nnd.saver_ae.restore(sess, nnd.path_to_ae_model)
rotation = sess.run(nnd.W_ae)
return rotation
def test_can_train_detector(path_to_config,
path_to_sample_pipeline_folder,
make_tmp_folder):
yass.set_config(path_to_config, make_tmp_folder)
CONFIG = yass.read_config()
spike_train = np.load(path.join(path_to_sample_pipeline_folder,
'spike_train.npy'))
chosen_templates = np.unique(spike_train[:, 1])
min_amplitude = 4
max_amplitude = 60
n_spikes_to_make = 100
templates = make.load_templates(path_to_sample_pipeline_folder,
spike_train, CONFIG, chosen_templates)
path_to_standardized = path.join(path_to_sample_pipeline_folder,
'preprocess', 'standarized.bin')
(x_detect, y_detect,
x_triage, y_triage,
x_ae, y_ae) = make.training_data(CONFIG, templates,
min_amplitude, max_amplitude,
n_spikes_to_make,
path_to_standardized)
_, waveform_length, n_neighbors = x_detect.shape
path_to_model = path.join(make_tmp_folder, 'detect-net.ckpt')
detector = NeuralNetDetector(path_to_model, [8, 4],
waveform_length, n_neighbors,
threshold=0.5,
channel_index=CONFIG.channel_index,
n_iter=10)
detector.fit(x_detect, y_detect)
def run_neural_network(standardized_path, standardized_params, whiten_filter,
output_directory, if_file_exists, save_results):
"""Run neural network detection and autoencoder dimensionality reduction
Returns
-------
scores
Scores for all spikes
spike_index_clear
Spike indexes for clear spikes
spike_index_all
Spike indexes for all spikes
"""
logger = logging.getLogger(__name__)
CONFIG = read_config()
TMP_FOLDER = CONFIG.path_to_output_directory
# check if all scores, clear and collision spikes exist..
path_to_score = os.path.join(TMP_FOLDER, 'scores_clear.npy')
path_to_spike_index_clear = os.path.join(TMP_FOLDER,
'spike_index_clear.npy')
path_to_spike_index_all = os.path.join(TMP_FOLDER, 'spike_index_all.npy')
path_to_rotation = os.path.join(TMP_FOLDER, 'rotation.npy')
path_to_standardized = os.path.join(TMP_FOLDER, 'preprocess',
'standarized.bin')
paths = [path_to_score, path_to_spike_index_clear, path_to_spike_index_all]
exists = [os.path.exists(p) for p in paths]
if (if_file_exists == 'overwrite' or not any(exists)):
max_memory = (CONFIG.resources.max_memory_gpu
if GPU_ENABLED else CONFIG.resources.max_memory)
# make tensorflow tensors and neural net classes
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
n_channels = CONFIG.recordings.n_channels
# open tensorflow for every chunk
NND = NeuralNetDetector.load(detection_fname, detection_th,
CONFIG.channel_index)
NNAE = AutoEncoder.load(ae_fname, input_tensor=NND.waveform_tf)
# run nn preprocess batch-wsie
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
# compute len of recording
filename_dat = os.path.join(CONFIG.data.root_folder,
CONFIG.data.recordings)
fp = np.memmap(filename_dat, dtype='int16', mode='r')
fp_len = fp.shape[0] / n_channels
# compute batch indexes
buffer_size = 200 # Cat: to set this in CONFIG file
sampling_rate = CONFIG.recordings.sampling_rate
#n_sec_chunk = CONFIG.resources.n_sec_chunk
# Cat: TODO: Set a different size chunk for clustering vs. detection
n_sec_chunk = 60
# take chunks
indexes = np.arange(0, fp_len, sampling_rate * n_sec_chunk)
# add last bit of recording if it's shorter
if indexes[-1] != fp_len:
indexes = np.hstack((indexes, fp_len))
idx_list = []
for k in range(len(indexes) - 1):
idx_list.append([
indexes[k], indexes[k + 1], buffer_size,
indexes[k + 1] - indexes[k] + buffer_size
])
idx_list = np.int64(np.vstack(idx_list))[:20]
#idx_list = idx_list
logger.info("# of chunks: %i", len(idx_list))
logger.info(idx_list)
# run tensorflow
processing_ctr = 0
#chunk_ctr = 0
# chunks to cycle over are 10 x as much as initial chosen data
total_processing = len(idx_list) * n_sec_chunk
# keep tensorflow open
# save iteratively
fname_detection = os.path.join(CONFIG.path_to_output_directory,
'detect')
if not os.path.exists(fname_detection):
os.mkdir(fname_detection)
# set tensorflow verbosity level
tf.logging.set_verbosity(tf.logging.ERROR)
# open etsnrflow session
with tf.Session() as sess:
#K.set_session(sess)
NND.restore(sess)
#triage = KerasModel(triage_fname,
# allow_longer_waveform_length=True,
# allow_more_channels=True)
# read chunks of data first:
# read chunk of raw standardized data
# Cat: TODO: don't save to lists, might want to use numpy arrays directl
#print (os.path.join(fname_detection,"detect_"+
# str(chunk_ctr).zfill(5)+'.npz'))
# loop over 10sec or 60 sec chunks
for chunk_ctr, idx in enumerate(idx_list):
if os.path.exists(
os.path.join(
fname_detection,
"detect_" + str(chunk_ctr).zfill(5) + '.npz')):
continue
# reset lists
spike_index_list = []
#idx_clean_list = []
energy_list = []
TC_list = []
offset_list = []
# load chunk of data
standardized_recording = binary_reader(idx, buffer_size,
path_to_standardized,
n_channels,
CONFIG.data.root_folder)
# run detection on smaller chunks of data, e.g. 1 sec
# Cat: TODO: add last bit at end in case short
indexes = np.arange(0, standardized_recording.shape[0],
sampling_rate)
# run tensorflow over 1sec chunks in general
for ctr, index in enumerate(indexes[:-1]):
# save absolute index of each subchunk
offset_list.append(idx[0] + indexes[ctr])
data_temp = standardized_recording[indexes[ctr]:indexes[ctr
+
1]]
# store size of recordings in case at end of dataset.
TC_list.append(data_temp.shape)
# run detect nn
res = NND.predict_recording(data_temp,
sess=sess,
output_names=('spike_index',
'waveform'))
spike_index, wfs = res
#idx_clean = (triage
# .predict_with_threshold(x=wfs,
# threshold=triage_th))
score = NNAE.predict(wfs, sess)
rot = NNAE.load_rotation(sess)
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels,
2)
# idx_clean is the indexes of clear spikes in all_spikes
spike_index_list.append(spike_index)
#idx_clean_list.append(idx_clean)
# run AE nn; required for remove_axon function
# Cat: TODO: Do we really need this: can we get energy list faster?
#rot = NNAE.load_rotation()
energy_ = np.ptp(np.matmul(score[:, :, 0], rot.T), axis=1)
energy_list.append(energy_)
logger.info('processed chunk: %s/%s, # spikes: %s',
str(processing_ctr), str(total_processing),
spike_index.shape)
processing_ctr += 1
# save chunk of data in case crashes occur
np.savez(os.path.join(fname_detection,
"detect_" + str(chunk_ctr).zfill(5)),
spike_index_list=spike_index_list,
energy_list=energy_list,
TC_list=TC_list,
offset_list=offset_list)
# load all saved data;
spike_index_list = []
energy_list = []
TC_list = []
offset_list = []
for ctr, idx in enumerate(idx_list):
data = np.load(fname_detection + '/detect_' + str(ctr).zfill(5) +
'.npz')
spike_index_list.extend(data['spike_index_list'])
energy_list.extend(data['energy_list'])
TC_list.extend(data['TC_list'])
offset_list.extend(data['offset_list'])
# save all detected spikes pre axon_kill
spike_index_all_pre_deduplication = fix_indexes_firstbatch_3(
spike_index_list, offset_list, buffer_size, sampling_rate)
np.save(
os.path.join(TMP_FOLDER, 'spike_index_all_pre_deduplication.npy'),
spike_index_all_pre_deduplication)
# remove axons - compute axons to be killed
logger.info(' removing axons in parallel')
multi_procesing = 1
if CONFIG.resources.multi_processing:
keep = parmap.map(deduplicate,
list(
zip(spike_index_list, energy_list, TC_list,
np.arange(len(energy_list)))),
neighbors,
processes=CONFIG.resources.n_processors,
pm_pbar=True)
else:
keep = []
for k in range(len(energy_list)):
keep.append(
deduplicate(
(spike_index_list[k], energy_list[k], TC_list[k], k),
neighbors))
# Cat: TODO Note that we're killing spike_index_all as well.
# remove axons from clear spikes - keep only non-killed+clean events
spike_index_all_postkill = []
for k in range(len(spike_index_list)):
spike_index_all_postkill.append(spike_index_list[k][keep[k][0]])
logger.info(' fixing indexes from batching')
spike_index_all = fix_indexes_firstbatch_3(spike_index_all_postkill,
offset_list, buffer_size,
sampling_rate)
# get and clean all spikes
spikes_all = spike_index_all
#logger.info('Removing all indexes outside the allowed range to '
# 'draw a complete waveform...')
_n_observations = fp_len
spikes_all, _ = detect.remove_incomplete_waveforms(
spikes_all, CONFIG.spike_size + CONFIG.templates.max_shift,
_n_observations)
np.save(os.path.join(TMP_FOLDER, 'spike_index_all.npy'), spikes_all)
else:
spikes_all = np.load(os.path.join(TMP_FOLDER, 'spike_index_all.npy'))
return spikes_all
def run_neural_network(standarized_path, standarized_params, whiten_filter,
output_directory, if_file_exists, save_results):
"""Run neural network detection and autoencoder dimensionality reduction
Returns
-------
scores
Scores for all spikes
spike_index_clear
Spike indexes for clear spikes
spike_index_all
Spike indexes for all spikes
"""
logger = logging.getLogger(__name__)
CONFIG = read_config()
folder = Path(CONFIG.data.root_folder, output_directory, 'detect')
folder.mkdir(exist_ok=True)
TMP_FOLDER = str(folder)
# check if all scores, clear and collision spikes exist..
path_to_score = os.path.join(TMP_FOLDER, 'scores_clear.npy')
path_to_spike_index_clear = os.path.join(TMP_FOLDER,
'spike_index_clear.npy')
path_to_spike_index_all = os.path.join(TMP_FOLDER, 'spike_index_all.npy')
path_to_rotation = os.path.join(TMP_FOLDER, 'rotation.npy')
paths = [path_to_score, path_to_spike_index_clear, path_to_spike_index_all]
exists = [os.path.exists(p) for p in paths]
if (if_file_exists == 'overwrite'
or if_file_exists == 'abort' and not any(exists)
or if_file_exists == 'skip' and not all(exists)):
max_memory = (CONFIG.resources.max_memory_gpu
if GPU_ENABLED else CONFIG.resources.max_memory)
# instantiate batch processor
bp = BatchProcessor(standarized_path,
standarized_params['dtype'],
standarized_params['n_channels'],
standarized_params['data_order'],
max_memory,
buffer_size=CONFIG.spike_size)
# load parameters
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
# instantiate neural networks
NND = NeuralNetDetector.load(detection_fname, detection_th,
CONFIG.channel_index)
NNT = NeuralNetTriage.load(triage_fname,
triage_th,
input_tensor=NND.waveform_tf)
NNAE = AutoEncoder(ae_fname, input_tensor=NND.waveform_tf)
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
rotation = NNAE.load_rotation()
# gather all output tensors
output_tf = (NNAE.score_tf, NND.spike_index_tf, NNT.idx_clean)
# run detection
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
mc = bp.multi_channel_apply
res = mc(neuralnetwork.run_detect_triage_featurize,
mode='memory',
cleanup_function=neuralnetwork.fix_indexes,
sess=sess,
x_tf=NND.x_tf,
output_tf=output_tf,
rot=rotation,
neighbors=neighbors)
# get clear spikes
clear = np.concatenate([element[1] for element in res], axis=0)
logger.info('Removing clear indexes outside the allowed range to '
'draw a complete waveform...')
clear, idx = detect.remove_incomplete_waveforms(
clear, CONFIG.spike_size + CONFIG.templates.max_shift,
bp.reader._n_observations)
# get all spikes
spikes_all = np.concatenate([element[2] for element in res], axis=0)
logger.info('Removing indexes outside the allowed range to '
'draw a complete waveform...')
spikes_all, _ = detect.remove_incomplete_waveforms(
spikes_all, CONFIG.spike_size + CONFIG.templates.max_shift,
bp.reader._n_observations)
# get scores
scores = np.concatenate([element[0] for element in res], axis=0)
logger.info('Removing scores for indexes outside the allowed range to '
'draw a complete waveform...')
scores = scores[idx]
# transform scores to location + shape feature space
# TODO: move this to another place
if CONFIG.cluster.method == 'location':
threshold = 2
scores = get_locations_features(scores, rotation, clear[:, 1],
CONFIG.channel_index, CONFIG.geom,
threshold)
idx_nan = np.where(np.isnan(np.sum(scores, axis=(1, 2))))[0]
scores = np.delete(scores, idx_nan, 0)
clear = np.delete(clear, idx_nan, 0)
# save partial results if required
if save_results:
# save clear spikes
np.save(path_to_spike_index_clear, clear)
logger.info('Saved spike index clear in {}...'.format(
path_to_spike_index_clear))
# save all ppikes
np.save(path_to_spike_index_all, spikes_all)
logger.info('Saved spike index all in {}...'.format(
path_to_spike_index_all))
# save rotation
np.save(path_to_rotation, rotation)
logger.info(
'Saved rotation matrix in {}...'.format(path_to_rotation))
# saves scores
np.save(path_to_score, scores)
logger.info('Saved spike scores in {}...'.format(path_to_score))
elif if_file_exists == 'abort' and any(exists):
conflict = [p for p, e in zip(paths, exists) if e]
message = reduce(lambda x, y: str(x) + ', ' + str(y), conflict)
raise ValueError('if_file_exists was set to abort, the '
'program halted since the following files '
'already exist: {}'.format(message))
elif if_file_exists == 'skip' and all(exists):
logger.warning('Skipped execution. All output files exist'
', loading them...')
scores = np.load(path_to_score)
clear = np.load(path_to_spike_index_clear)
spikes_all = np.load(path_to_spike_index_all)
else:
raise ValueError(
'Invalid value for if_file_exists {}'
'must be one of overwrite, abort or skip'.format(if_file_exists))
return scores, clear, spikes_all
def prepare_nn(channel_index, whiten_filter, threshold_detect,
threshold_triage, detector_filename, autoencoder_filename,
triage_filename):
"""Prepare neural net tensors in advance. This is to effciently run
neural net with batch processor as we don't have to recreate tf
tensors in every batch
Parameters
----------
channel_index: np.array (n_channels, n_neigh)
Each row indexes its neighboring channels.
For example, channel_index[c] is the index of
neighboring channels (including itself)
If any value is equal to n_channels, it is nothing but
a space holder in a case that a channel has less than
n_neigh neighboring channels
whiten_filter: numpy.ndarray (n_channels, n_neigh, n_neigh)
whitening matrix such that whiten_filter[c] is the whitening
filter of channel c and its neighboring channel determined from
channel_index.
threshold_detect: int
threshold for neural net detection
threshold_triage: int
threshold for neural net triage
detector_filename: str
location of trained neural net detectior
autoencoder_filename: str
location of trained neural net autoencoder
triage_filename: str
location of trained neural net triage
Returns
-------
x_tf: tf.tensors (n_observations, n_channels)
placeholder of recording for running tensorflow
output_tf: tuple of tf.tensors
a tuple of tensorflow tensors that produce score, spike_index_clear,
and spike_index_collision
NND: class
an instance of class, NeuralNetDetector
NNT: class
an instance of class, NeuralNetTriage
"""
# placeholder for input recording
x_tf = tf.placeholder("float", [None, None])
# load Neural Net's
NND = NeuralNetDetector(detector_filename)
NNAE = AutoEncoder(autoencoder_filename)
NNT = NeuralNetTriage(triage_filename)
# make spike_index tensorflow tensor
spike_index_tf_all = NND.make_detection_tf_tensors(x_tf, channel_index,
threshold_detect)
# remove edge spike time
spike_index_tf = remove_edge_spikes(x_tf, spike_index_tf_all,
NND.filters_dict['size'])
# make waveform tensorflow tensor
waveform_tf = make_waveform_tf_tensor(x_tf, spike_index_tf, channel_index,
NND.filters_dict['size'])
# make score tensorflow tensor from waveform
score_tf = NNAE.make_score_tf_tensor(waveform_tf)
# run neural net triage
nneigh = NND.filters_dict['n_neighbors']
idx_clean = NNT.triage_wf(waveform_tf[:, :, :nneigh], threshold_triage)
# gather all output tensors
output_tf = (score_tf, spike_index_tf, idx_clean)
return x_tf, output_tf, NND, NNAE, NNT
def nn_detection(recordings, neighbors, geom, temporal_features,
temporal_window, th_detect, th_triage, detector_filename,
autoencoder_filename, triage_filename):
"""Detect spikes using a neural network
Parameters
----------
recordings: numpy.ndarray (n_observations, n_channels)
Neural recordings
neighbors: numpy.ndarray (n_channels, n_channels)
Channels neighbors matric
geom: numpy.ndarray (n_channels, 2)
Cartesian coordinates for the channels
temporal_features: int
?
temporal_window: int
?
th_detect: float?
Spike threshold [improve this explanation]
th_triage: float?
Triage threshold [improve this explanation]
detector_filename: str
Path to neural network detector
autoencoder_filename: str
Path to neural network autoencoder
triage_filename: str
Path to triage neural network
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_collision: numpy.ndarray (n_collided_spikes, 2)
2D array with indexes for collided spikes, first column contains the
spike location in the recording and the second the main channel
(channel whose amplitude is maximum)
"""
nnd = NeuralNetDetector(detector_filename, autoencoder_filename)
nnt = NeuralNetTriage(triage_filename)
T, C = recordings.shape
a, b = neighbors.shape
if a != b:
raise ValueError('neighbors is not a square matrix, verify')
if a != C:
raise ValueError(
'Number of channels in recording are {} but the '
'neighbors matrix has {} elements, they must match'.format(C, a))
# neighboring channel info
nneigh = np.max(np.sum(neighbors, 0))
c_idx = np.ones((C, nneigh), 'int32') * C
for c in range(C):
ch_idx, temp = order_channels_by_distance(c,
np.where(neighbors[c])[0],
geom)
c_idx[c, :ch_idx.shape[0]] = ch_idx
# input
x_tf = tf.placeholder("float", [T, C])
# detect spike index
local_max_idx_tf = nnd.get_spikes(x_tf, T, nneigh, c_idx, temporal_window,
th_detect)
# get score train
score_train_tf = nnd.get_score_train(x_tf)
# get energy for detected index
energy_tf = tf.reduce_sum(tf.square(score_train_tf), axis=2)
energy_val_tf = tf.gather_nd(energy_tf, local_max_idx_tf)
# get triage probability
triage_prob_tf = nnt.triage_prob(x_tf, T, nneigh, c_idx)
# gather all results above
result = (local_max_idx_tf, score_train_tf, energy_val_tf, triage_prob_tf)
# remove duplicates
energy_train_tf = tf.placeholder("float", [T, C])
spike_index_tf = remove_duplicate_spikes_by_energy(energy_train_tf, T,
c_idx, temporal_window)
# get score
score_train_placeholder = tf.placeholder("float",
[T, C, temporal_features])
spike_index_clear_tf = tf.placeholder("int64", [None, 2])
score_tf = get_score(score_train_placeholder, spike_index_clear_tf, T,
temporal_features, c_idx)
###############################
# get values of above tensors #
###############################
with tf.Session() as sess:
nnd.saver.restore(sess, nnd.path_to_detector_model)
nnd.saver_ae.restore(sess, nnd.path_to_ae_model)
nnt.saver.restore(sess, nnt.path_to_triage_model)
local_max_idx, score_train, energy_val, triage_prob = sess.run(
result, feed_dict={x_tf: recordings})
energy_train = np.zeros((T, C))
energy_train[local_max_idx[:, 0], local_max_idx[:, 1]] = energy_val
spike_index = sess.run(spike_index_tf,
feed_dict={energy_train_tf: energy_train})
idx_clean = triage_prob[spike_index[:, 0], spike_index[:,
1]] > th_triage
spike_index_clear = spike_index[idx_clean]
spike_index_collision = spike_index[~idx_clean]
score = sess.run(score_tf,
feed_dict={
score_train_placeholder: score_train,
spike_index_clear_tf: spike_index_clear
})
return score, spike_index_clear, spike_index_collision
def test_splitting_in_batches_does_not_affect(path_to_config,
path_to_sample_pipeline_folder,
make_tmp_folder,
path_to_standardized_data):
yass.set_config(path_to_config, make_tmp_folder)
CONFIG = yass.read_config()
PATH_TO_DATA = path_to_standardized_data
with open(path.join(path_to_sample_pipeline_folder, 'preprocess',
'standardized.yaml')) as f:
PARAMS = yaml.load(f)
channel_index = make_channel_index(CONFIG.neigh_channels,
CONFIG.geom)
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
# instantiate neural networks
NND = NeuralNetDetector.load(detection_fname, detection_th,
channel_index)
triage = KerasModel(triage_fname,
allow_longer_waveform_length=True,
allow_more_channels=True)
NNAE = AutoEncoder.load(ae_fname, input_tensor=NND.waveform_tf)
bp = BatchProcessor(PATH_TO_DATA, PARAMS['dtype'], PARAMS['n_channels'],
PARAMS['data_order'], '100KB',
buffer_size=CONFIG.spike_size)
out = ('spike_index', 'waveform')
fn = neuralnetwork.apply.fix_indexes_spike_index
# detector
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
res = bp.multi_channel_apply(NND.predict_recording,
mode='memory',
sess=sess,
output_names=out,
cleanup_function=fn)
spike_index_new = np.concatenate([element[0] for element in res], axis=0)
wfs = np.concatenate([element[1] for element in res], axis=0)
idx_clean = triage.predict_with_threshold(wfs, triage_th)
score = NNAE.predict(wfs)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
(score_clear_new,
spike_index_clear_new) = post_processing(score,
spike_index_new,
idx_clean,
rot,
neighbors)
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
res = bp.multi_channel_apply(NND.predict_recording,
mode='memory',
sess=sess,
output_names=('spike_index',
'waveform'),
cleanup_function=fn)
spike_index_batch, wfs = zip(*res)
spike_index_batch = np.concatenate(spike_index_batch, axis=0)
wfs = np.concatenate(wfs, axis=0)
idx_clean = triage.predict_with_threshold(x=wfs,
threshold=triage_th)
score = NNAE.predict(wfs)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
(score_clear_batch,
spike_index_clear_batch) = post_processing(score,
spike_index_batch,
idx_clean,
rot,
neighbors)
def test_splitting_in_batches_does_not_affect(path_to_tests,
path_to_standarized_data,
path_to_sample_pipeline_folder):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
PATH_TO_DATA = path_to_standarized_data
data = RecordingsReader(PATH_TO_DATA, loader='array').data
with open(
path.join(path_to_sample_pipeline_folder, 'preprocess',
'standarized.yaml')) as f:
PARAMS = yaml.load(f)
channel_index = make_channel_index(CONFIG.neigh_channels, CONFIG.geom)
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
# instantiate neural networks
NND = NeuralNetDetector.load(detection_fname, detection_th, channel_index)
NNT = NeuralNetTriage.load(triage_fname,
triage_th,
input_tensor=NND.waveform_tf)
NNAE = AutoEncoder(ae_fname, input_tensor=NND.waveform_tf)
output_tf = (NNAE.score_tf, NND.spike_index_tf, NNT.idx_clean)
# run all at once
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
(scores, clear, collision) = neuralnetwork.run_detect_triage_featurize(
data, sess, NND.x_tf, output_tf, neighbors, rot)
# run in batches - buffer size makes sure we can detect spikes if they
# appear at the end of any batch
bp = BatchProcessor(PATH_TO_DATA,
PARAMS['dtype'],
PARAMS['n_channels'],
PARAMS['data_order'],
'100KB',
buffer_size=CONFIG.spike_size)
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
res = bp.multi_channel_apply(
neuralnetwork.run_detect_triage_featurize,
mode='memory',
cleanup_function=neuralnetwork.fix_indexes,
sess=sess,
x_tf=NND.x_tf,
output_tf=output_tf,
rot=rot,
neighbors=neighbors)
scores_batch = np.concatenate([element[0] for element in res], axis=0)
clear_batch = np.concatenate([element[1] for element in res], axis=0)
collision_batch = np.concatenate([element[2] for element in res], axis=0)
np.testing.assert_array_equal(clear_batch, clear)
np.testing.assert_array_equal(collision_batch, collision)
np.testing.assert_array_equal(scores_batch, scores)
