def threshold_amplitude_cutoffs(sorting,
recording,
threshold,
threshold_sign,
recording_params=get_recording_params(),
amplitude_params=get_amplitude_params(),
feature_params=get_feature_params(),
save_as_property=True,
seed=AmplitudeCutoff.params['seed'],
verbose=AmplitudeCutoff.params['verbose']):
"""
Computes and thresholds the amplitude cutoffs in the sorted dataset with the given sign and value.
Parameters
----------
sorting: SortingExtractor
The sorting result to be evaluated.
recording: RecordingExtractor
The given recording extractor
threshold: int or float
The threshold for the given metric.
threshold_sign: str
If 'less', will threshold any metric less than the given threshold.
If 'less_or_equal', will threshold any metric less than or equal to the given threshold.
If 'greater', will threshold any metric greater than the given threshold.
If 'greater_or_equal', will threshold any metric greater than or equal to the given threshold.
amplitude_params: dict
This dictionary should contain any subset of the following parameters:
amp_method: str
If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned.
If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes.
amp_peak: str
If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default)
amp_frames_before: int
Frames before peak to compute amplitude.
amp_frames_after: int
Frames after peak to compute amplitude.
recording_params: dict
This dictionary should contain any subset of the following parameters:
apply_filter: bool
If True, recording is bandpass-filtered.
freq_min: float
High-pass frequency for optional filter (default 300 Hz).
freq_max: float
Low-pass frequency for optional filter (default 6000 Hz).
feature_params: dict
This dictionary should contain any subset of the following parameters:
save_features_props: bool
If true, it will save features in the sorting extractor.
recompute_info: bool
If True, waveforms are recomputed
max_spikes_per_unit: int
The maximum number of spikes to extract per unit.
save_as_property: bool
If True, the metric is saved as sorting property
seed: int
Random seed for reproducibility
verbose: bool
If True, will be verbose in metric computation.
Returns
----------
threshold sorting extractor
"""
rp_dict, ap_dict, fp_dict = update_param_dicts(
recording_params=recording_params,
amplitude_params=amplitude_params,
feature_params=feature_params)
md = MetricData(sorting=sorting,
sampling_frequency=recording.get_sampling_frequency(),
recording=recording,
apply_filter=rp_dict["apply_filter"],
freq_min=rp_dict["freq_min"],
freq_max=rp_dict["freq_max"],
unit_ids=None,
epoch_tuples=None,
epoch_names=None,
verbose=verbose)
md.compute_amplitudes(
amp_method=ap_dict["amp_method"],
amp_peak=ap_dict["amp_peak"],
amp_frames_before=ap_dict["amp_frames_before"],
amp_frames_after=ap_dict["amp_frames_after"],
max_spikes_per_unit=fp_dict["max_spikes_per_unit"],
save_features_props=fp_dict['save_features_props'],
recompute_info=fp_dict['recompute_info'],
seed=seed,
)
ac = AmplitudeCutoff(metric_data=md)
threshold_sorting = ac.threshold_metric(threshold, threshold_sign,
save_as_property)
return threshold_sorting
def threshold_amplitude_cutoffs(
sorting,
recording,
threshold,
threshold_sign,
**kwargs
):
"""
Computes and thresholds the amplitude cutoffs in the sorted dataset with the given sign and value.
Parameters
----------
sorting: SortingExtractor
The sorting result to be evaluated
recording: RecordingExtractor
The given recording extractor
threshold: int or float
The threshold for the given metric
threshold_sign: str
If 'less', will threshold any metric less than the given threshold
If 'less_or_equal', will threshold any metric less than or equal to the given threshold
If 'greater', will threshold any metric greater than the given threshold
If 'greater_or_equal', will threshold any metric greater than or equal to the given threshold
**kwargs: keyword arguments
Keyword arguments among the following:
method: str
If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned
If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes
peak: str
If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or
both ('both' - default)
frames_before: int
Frames before peak to compute amplitude
frames_after: int
Frames after peak to compute amplitude
apply_filter: bool
If True, recording is bandpass-filtered
freq_min: float
High-pass frequency for optional filter (default 300 Hz)
freq_max: float
Low-pass frequency for optional filter (default 6000 Hz)
save_property_or_features: bool
If true, it will save features in the sorting extractor
recompute_info: bool
If True, waveforms are recomputed
max_spikes_per_unit: int
The maximum number of spikes to extract per unit
Returns
----------
threshold sorting extractor
"""
params_dict = update_all_param_dicts_with_kwargs(kwargs)
md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording,
apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"],
duration_in_frames=None, freq_max=params_dict["freq_max"], unit_ids=None, verbose=params_dict['verbose'])
md.compute_amplitudes(**kwargs)
ac = AmplitudeCutoff(metric_data=md)
threshold_sorting = ac.threshold_metric(threshold, threshold_sign, **kwargs)
return threshold_sorting