Metrics module

The metrics module includes functions to compute various metrics related to spike sorting.

Currently, it contains the following submodules:

• template metrics: Computes commonly used waveform/template metrics.

• quality metrics: Computes a variety of quality metrics to assess the goodness of spike sorting outputs.

• spiketrain metrics: Computes metrics based on spike train statistics and correlogram shapes.

All metrics extensions inherit from the BaseMetricExtension base class, which provides a common interface for computing and retrieving metrics and has convenience method to access metric information. For example, you can get the list of available metrics and their descriptions with:

from spikeinterface.metrics import ComputeTemplateMetrics
# ComputeTemplateMetrics inherits from BaseMetricExtension

available_metric_columns = ComputeTemplateMetrics.get_metric_columns()
print(f"Available metric columns: ")
print(available_metric_columns)

Available metric columns:
[
    'peak_to_trough_duration',
    'trough_half_width',
    'peak_half_width',
    'repolarization_slope',
    'recovery_slope',
    'num_positive_peaks',
    'num_negative_peaks',
    'main_to_next_peak_duration',
    'peak_before_to_trough_ratio',
    'peak_after_to_trough_ratio',
    'peak_before_to_peak_after_ratio',
    'main_peak_to_trough_ratio',
    'trough_width',
    'peak_before_width',
    'peak_after_width',
    'waveform_baseline_flatness',
    'velocity_above',
    'velocity_below',
    'exp_decay',
    'spread'
]

metric_descriptions = ComputeTemplateMetrics.get_metric_column_descriptions()
print("Metric descriptions: ")
print(metric_descriptions)

Metric descriptions:
{
'exp_decay': 'Spatial decay of the template amplitude over distance from the '
             'extremum channel (1/um). Uses exponential or linear fit based '
             'on linear_fit parameter.',
'main_peak_to_trough_ratio': 'Ratio of main peak amplitude to trough amplitude',
'main_to_next_extremum_duration': 'Duration in seconds from main extremum to next extremum.',
'num_negative_peaks': 'Number of negative peaks (troughs) in the template',
'num_positive_peaks': 'Number of positive peaks in the template',
'peak_after_to_trough_ratio': 'Ratio of peak after amplitude to trough amplitude',
'peak_after_width': 'Width of the main peak after trough in seconds',
'peak_before_to_peak_after_ratio': 'Ratio of peak before amplitude to peak after amplitude',
'peak_before_to_trough_ratio': 'Ratio of peak before amplitude to trough amplitude',
'peak_before_width': 'Width of the main peak before trough in seconds',
'peak_half_width': 'Duration in s at half the amplitude of the peak (maximum) of the template.',
'peak_to_trough_duration': 'Duration in seconds between the trough (minimum) and the next peak (maximum) of the template.',
'recovery_slope': 'Slope of the recovery phase of the template, after the peak (maximum) returning to baseline in uV/s.',
'repolarization_slope': 'Slope of the repolarization phase of the template, between the trough (minimum) and return to baseline in uV/s.',
'spread': 'Spread of the template amplitude in um, calculated as the distance between channels whose templates exceed the spread_threshold.',
'trough_half_width': 'Duration in s at half the amplitude of the trough (minimum) of the template.',
'trough_width': 'Width of the main trough in seconds',
'velocity_above': 'Velocity of the spike propagation above the max channel in um/ms',
'velocity_below': 'Velocity of the spike propagation below the max channel in um/ms',
'waveform_baseline_flatness': 'Ratio of max baseline amplitude to max waveform amplitude. Lower = flatter baseline.'

}

Metrics submodules

• Template Metrics
• Quality Metrics module
• Spike Train Metrics