def test_can_use_detector_after_fit(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()
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)
data = RecordingExplorer(path_to_standardized_data).reader.data
output_names = ('spike_index', 'waveform', 'probability')
(spike_index, waveform,
proba) = detector.predict_recording(data, output_names=output_names)
detector.predict(x_detect)
def test_can_reload_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_standarized = 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_standarized)
_, 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)
NeuralNetDetector.load(path_to_model, threshold=0.5,
channel_index=CONFIG.channel_index)