Augment¶
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yass.augment.make.load_templates(data_folder, spike_train, CONFIG, chosen_templates_indexes)[source]¶ Parameters: - data_folder: str
Folder storing the standarized data (if not exist, run preprocess to automatically generate)
- spike_train: numpy.ndarray
[number of spikes, 2] Ground truth for training. First column is the spike time, second column is the spike id
- chosen_templates_indexes: list
List of chosen templates’ id’s
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yass.augment.make.spikes(templates, min_amplitude, max_amplitude, n_per_template, spatial_sig, temporal_sig, make_from_templates=True, make_spatially_misaligned=True, make_temporally_misaligned=True, make_collided=True, make_noise=True, return_metadata=True, templates_kwargs={}, collided_kwargs={'min_shift': 5, 'n_per_spike': 1}, temporally_misaligned_kwargs={'n_per_spike': 1}, spatially_misaligned_kwargs={'force_first_channel_shuffle': True, 'n_per_spike': 1}, add_noise_kwargs={'reject_cancelling_noise': False})[source]¶ Make spikes, it creates several types of spikes from templates with a range of amplitudes
Parameters: - templates: numpy.ndarray, (n_templates, waveform_length, n_channels)
Templates used to generate the spikes
- min_amplitude: float
Minimum amplitude for the spikes
- max_amplitude: float
Maximum amplitude for the spikes
- n_per_template: int
How many spikes to generate per template. This along with min_amplitude and max_amplitude are used to generate spikes covering the desired amplitude range
- make_from_templates: bool
Whether to return spikes generated from the templates (these are the same as the templates but with different amplitudes)
- make_spatially_misaligned: bool
Whether to return spatially misaligned spikes (by shuffling channels)
- make_temporally_misaligned: bool
Whether to return temporally misaligned spikes (by shifting along the temporal axis)
- make_collided: bool
Whether to return collided spikes
- make_noise: bool
Whether to return pure noise
- return_metadata: bool, optional
Return metadata in the generated spikes
Returns: - x_all: numpy.ndarray, (n_templates * n_per_template, waveform_length,
- n_channels)
All generated spikes
- x_all_noisy: numpy.ndarray, (n_templates * n_per_template, waveform_length,
- n_channels)
Noisy versions of all generated spikes
- the_amplitudes: numpy.ndarray, (n_templates * n_per_template,)
Amplitudes for all generated spikes
- slices: dictionary
Dictionary where the keys are the kind of spikes (‘from templates’, ‘spatially misaligned’, ‘temporally misaligned’, ‘collided’, noise’) and the values are slice objects with the location for each kind of spike
- spatial_SIG
- temporal_SIG
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yass.augment.make.training_data(CONFIG, templates_uncropped, min_amp, max_amp, n_isolated_spikes, path_to_standarized, noise_ratio=10, collision_ratio=1, misalign_ratio=1, misalign_ratio2=1, multi_channel=True, return_metadata=False)[source]¶ Makes training sets for detector, triage and autoencoder
Parameters: - CONFIG: yaml file
Configuration file
- min_amp: float
Minimum value allowed for the maximum absolute amplitude of the isolated spike on its main channel
- max_amp: float
Maximum value allowed for the maximum absolute amplitude of the isolated spike on its main channel
- n_isolated_spikes: int
Number of isolated spikes to generate. This is different from the total number of x_detect
- path_to_standarized: str
Folder storing the standarized data (if not exist, run preprocess to automatically generate)
- noise_ratio: int
Ratio of number of noise to isolated spikes. For example, if n_isolated_spike=1000, noise_ratio=5, then n_noise=5000
- collision_ratio: int
Ratio of number of collisions to isolated spikes.
- misalign_ratio: int
Ratio of number of spatially and temporally misaligned spikes to isolated spikes
- misalign_ratio2: int
Ratio of number of only-spatially misaligned spikes to isolated spikes
- multi_channel: bool
If True, generate training data for multi-channel neural network. Otherwise generate single-channel data
Returns: - x_detect: numpy.ndarray
[number of detection training data, temporal length, number of channels] Training data for the detect net.
- y_detect: numpy.ndarray
[number of detection training data] Label for x_detect
- x_triage: numpy.ndarray
[number of triage training data, temporal length, number of channels] Training data for the triage net.
- y_triage: numpy.ndarray
[number of triage training data] Label for x_triage
- x_ae: numpy.ndarray
[number of ae training data, temporal length] Training data for the autoencoder: noisy spikes
- y_ae: numpy.ndarray
[number of ae training data, temporal length] Denoised x_ae
Notes
- Detection training data
- Multi channel
- Positive examples: Clean spikes + noise, Collided spikes + noise
- Negative examples: Temporally misaligned spikes + noise, Noise
- Triage training data
- Multi channel
- Positive examples: Clean spikes + noise
- Negative examples: Collided spikes + noise
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yass.augment.make.training_data_detect(templates, minimum_amplitude, maximum_amplitude, n_clean_per_template, n_collided_per_spike, n_temporally_misaligned_per_spike, n_spatially_misaliged_per_spike, n_noise, spatial_SIG, temporal_SIG, from_templates_kwargs={}, collided_kwargs={}, temporally_misaligned_kwargs={}, add_noise_kwargs={'reject_cancelling_noise': False})[source]¶ Make training data for detector network
Notes
Recordings are passed through the detector network which identifies spikes (clean and collided), it rejects noise and misaligned spikes (temporally and spatially)