# Postprocessing module¶

After spike sorting, we can use the `postprocessing`

module to further post-process
the spike sorting output. Most of the post-processing functions require a
`WaveformExtractor`

as input.

## WaveformExtractor extensions¶

There are several postprocessing tools available, and all
of them are implemented as a `BaseWaveformExtractorExtension`

. All computations on top
of a `WaveformExtractor`

will be saved along side the `WaveformExtractor`

itself (sub folder, zarr path or sub dict).
This workflow is convenient for retrieval of time-consuming computations (such as pca or spike amplitudes) when reloading a
`WaveformExtractor`

.

`BaseWaveformExtractorExtension`

objects are tightly connected to the
parent `WaveformExtractor`

object, so that operations done on the `WaveformExtractor`

, such as saving,
loading, or selecting units, will be automatically applied to all extensions.

To check what extensions are available for a `WaveformExtractor`

named `we`

, you can use:

```
import spikeinterface as si
available_extension_names = we.get_available_extension_names()
print(available_extension_names)
```

```
["principal_components", "spike_amplitudes"]
```

In this case, for example, principal components and spike amplitudes have already been computed. To load the extension object you can run:

```
ext = we.load_extension("spike_amplitudes")
ext_data = ext.get_data()
```

Here `ext`

is the extension object (in this case the `SpikeAmplitudeCalculator`

), and `ext_data`

will
contain the actual amplitude data. Note that different extensions might have different ways to return the extension.
You can use `ext.get_data?`

for documentation.

We can also delete an extension:

```
we.delete_extension("spike_amplitudes")
```

Finally, the waveform extensions can be loaded rather than recalculated by using the `load_if_exists`

argument in
any post-processing function.

## Available postprocessing extensions¶

### noise_levels¶

This extension computes the noise level of each channel using the median absolute deviation.
As an extension, this expects the `WaveformExtractor`

as input and the computed values are persistent on disk.

The `get_noise_levels(recording)()`

computes the same values, but starting from a recording
and without saving the data as an extension.

```
noise = compute_noise_level(waveform_extractor=we)
```

For more information, see `compute_noise_levels()`

### principal_components¶

This extension computes the principal components of the waveforms. There are several modes available:

“by_channel_local” (default): fits one PCA model for each by_channel

“by_channel_global”: fits the same PCA model to all channels (also termed temporal PCA)

“concatenated”: concatenates all channels and fits a PCA model on the concatenated data

If the input `WaveformExtractor`

is sparse, the sparsity is used when computing the PCA.
For dense waveforms, sparsity can also be passed as an argument.

```
pc = compute_principal_components(waveform_extractor=we,
n_components=3,
mode="by_channel_local")
```

For more information, see `compute_principal_components()`

### template_similarity¶

This extension computes the similarity of the templates to each other. This information could be used for automatic merging. Currently, the only available similarity method is the cosine similarity, which is the angle between the high-dimensional flattened template arrays. Note that cosine similarity does not take into account amplitude differences and is not well suited for high-density probes.

```
similarity = compute_template_similarity(waveform_extractor=we, method='cosine_similarity')
```

For more information, see `compute_template_similarity()`

### spike_amplitudes¶

This extension computes the amplitude of each spike as the value of the traces on the extremum channel at the times of each spike.

**NOTE:** computing spike amplitudes is highly recommended before calculating amplitude-based quality metrics, such as
Amplitude cutoff (amplitude_cutoff) and Amplitude median (amplitude_median).

```
amplitudes = computer_spike_amplitudes(waveform_extractor=we,
peak_sign="neg",
outputs="concatenated")
```

For more information, see `compute_spike_amplitudes()`

### spike_locations¶

This extension estimates the location of each spike in the sorting output. Spike location estimates can be done
with center of mass (`method="center_of_mass"`

- fast, but less accurate), a monopolar triangulation
(`method="monopolar_triangulation"`

- slow, but more accurate), or with the method of grid convolution
(`method="grid_convolution"`

)

**NOTE:** computing spike locations is required to compute Drift metrics (drift_ptp, drift_std, drift_mad).

```
spike_locations = compute_spike_locations(waveform_extractor=we,
ms_before=0.5,
ms_after=0.5,
spike_retriever_kwargs=dict(
channel_from_template=True,
radius_um=50,
peak_sign="neg"
),
method="center_of_mass")
```

For more information, see `compute_spike_locations()`

### unit_locations¶

This extension is similar to the `spike_locations`

, but instead of estimating a location for each spike
based on individual waveforms, it calculates at the unit level using templates. The same localization methods
(`method="center_of_mass" | "monopolar_triangulation" | "grid_convolution"`

) are available.

```
unit_locations = compute_unit_locations(waveform_extractor=we,
method="monopolar_triangulation")
```

For more information, see `compute_unit_locations()`

### template_metrics¶

This extension computes commonly used waveform/template metrics. By default, the following metrics are computed:

“peak_to_valley”: duration between negative and positive peaks

“halfwidth”: duration in s at 50% of the amplitude

“peak_to_trough_ratio”: ratio between negative and positive peaks

“recovery_slope”: speed in V/s to recover from the negative peak to 0

“repolarization_slope”: speed in V/s to repolarize from the positive peak to 0

“num_positive_peaks”: the number of positive peaks

“num_negative_peaks”: the number of negative peaks

Optionally, the following multi-channel metrics can be computed by setting:
`include_multi_channel_metrics=True`

“velocity_above”: the velocity above the max channel of the template

“velocity_below”: the velocity below the max channel of the template

“exp_decay”: the exponential decay of the template amplitude over distance

“spread”: the spread of the template amplitude over distance

For more information, see `compute_template_metrics()`

### correlograms¶

This extension computes correlograms (both auto- and cross-) for spike trains. The computed output is a 3d array with shape (num_units, num_units, num_bins) with all correlograms for each pair of units (diagonals are auto-correlograms).

```
ccgs, bins = compute_correlograms(waveform_or_sorting_extractor=we,
window_ms=50.0,
bin_ms=1.0,
method="auto")
```

For more information, see `compute_correlograms()`

### isi_histograms¶

This extension computes the histograms of inter-spike-intervals. The computed output is a 2d array with shape (num_units, num_bins), with the isi histogram of each unit.

```
isi_histogram, bins = compute_isi_histograms(waveform_or_sorting_extractor=we,
window_ms=50.0,
bin_ms=1.0,
method="auto")
```

For more information, see `compute_isi_histograms()`

## Other postprocessing tools¶

### align_sorting¶

This function aligns the spike trains `BaseSorting`

object using pre-computed shifts of misaligned templates.
To compute shifts, one can use the `get_template_extremum_channel_peak_shift()`

function.

For more information, see `align_sorting()`