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[![Build Status](https://travis-ci.com/SpikeInterface/spikemetrics.svg?branch=master)](https://travis-ci.com/SpikeInterface/spikemetrics) [![PyPI version](https://badge.fury.io/py/spikemetrics.svg)](https://badge.fury.io/py/spikemetrics)
# spikemetrics (LEGACY)

# SpikeMetrics
Computes quality metrics for sorted units. This module can calculate metrics separately for individual epochs. If no epochs are specified, metrics are computed for the entire recording.
The `spikemetrics` package has now been integrated into [spikeinterface](https://github.com/SpikeInterface/spikeinterface).

The base code (and portions of the README images/description) was ported from: https://github.com/AllenInstitute/ecephys_spike_sorting/tree/master/ecephys_spike_sorting/modules/quality_metrics
This package will be maintained for a while for bug fixes only, then it will be deprecated.

Copyright 2019. Allen Institute. All rights reserved.

## Included Metrics

| Metric | Icon | Description | Reference |
| ------------------ |:------------------------:| -------------------------------------------------- | -----------------|
| Spike Count | | Spike count in an epoch | |
| Firing rate | | Mean spike rate in an epoch | |
| Presence ratio | | Fraction of epoch in which spikes are present | |
| Amplitude cutoff |![](images/amp_cut.png) | Estimate of miss rate based on amplitude histogram | |
| ISI violations |![](images/isi_viol.png) | Rate of refractory-period violations | |
| Isolation distance |![](images/isol_dist.png) | The Mahalanobis distance from a specified unit within as many spikes belong to the specified unit as to other units | Harris et al. Neuron 32.1 (2001): 141-149. |
| L-ratio | | The Mahalanobis distance and chi-squared inverse cdf (given the assumption that the spikes in the cluster distribute normally in each dimension) are used to find the probability of cluster membership for each spike. | Schmitzer-Torbert and Redish. _J Neurophy_ 91.5 (2004): 2259-2272. |
| _d'_ |![](images/d_prime.png) | The classification accuracy between units based on linear discriminant analysis (LDA). | Hill et al. (2011) _J Neurosci_ **31**, 8699-9705 |
| Nearest-neighbors |![](images/nn_overlap.png)| Non-parametric estimate of unit contamination using nearest-neighbor classification. | Chung et al. (2017) _Neuron_ **95**, 1381-1394 |
| Silhouette score | | A standard metric for quantifying cluster overlap | |
| Maximum drift | | Maximum change in spike position throughout recording | |
| Cumulative drift | | Cumulative change in spike position throughout recording | |

### A Note on Calculations

For metrics based on waveform principal components (isolation distance, L-ratio, _d'_, and nearest neighbors hit rate and false alarm rate), it is typical to compute the metrics for all pairs of units and report the "worst-case" value. We have found that this tends to under- or over-estimate the degree of contamination when there are large firing rate differences between pairs of units that are being compared. Instead, we compute metrics by sub-selecting spikes from _all_ other units on the same set of channels, which seems to give a more accurate picture of isolation quality. We would appreciate feedback on whether this approach makes sense.
New features and improvements will only be implemented for the `spikeinterface` package.