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 using the 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