.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/metrics/plot_3_quality_metrics.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_tutorials_metrics_plot_3_quality_metrics.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials_metrics_plot_3_quality_metrics.py:


Quality Metrics Tutorial
========================

After spike sorting, you might want to validate the 'goodness' of the sorted units. This can be done using the
:code:`qualitymetrics` submodule, which computes several quality metrics of the sorted units.

.. GENERATED FROM PYTHON SOURCE LINES 9-17

.. code-block:: Python


    import spikeinterface.core as si
    from spikeinterface.metrics import (
        compute_snrs,
        compute_presence_ratios,
        compute_isi_violations,
    )








.. GENERATED FROM PYTHON SOURCE LINES 18-19

First, let's generate a simulated recording and sorting

.. GENERATED FROM PYTHON SOURCE LINES 19-24

.. code-block:: Python


    recording, sorting = si.generate_ground_truth_recording()
    print(recording)
    print(sorting)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    GroundTruthRecording (InjectTemplatesRecording): 4 channels - 25.0kHz - 1 segments 
                          250,000 samples - 10.00s - float32 dtype - 3.81 MiB
    GroundTruthSorting (NumpySorting): 10 units - 1 segments - 25.0kHz




.. GENERATED FROM PYTHON SOURCE LINES 25-29

Create SortingAnalyzer
-----------------------

For quality metrics we need first to create a :code:`SortingAnalyzer`.

.. GENERATED FROM PYTHON SOURCE LINES 29-33

.. code-block:: Python


    analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording, format="memory")
    print(analyzer)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    SortingAnalyzer: 4 channels - 10 units - 1 segments - memory - sparse - has recording
    Loaded 0 extensions




.. GENERATED FROM PYTHON SOURCE LINES 34-36

Depending on which metrics we want to compute we will need first to compute
some necessary extensions. (if not computed an error message will be raised)

.. GENERATED FROM PYTHON SOURCE LINES 36-45

.. code-block:: Python


    analyzer.compute("random_spikes", method="uniform", max_spikes_per_unit=600, seed=2205)
    analyzer.compute("waveforms", ms_before=1.3, ms_after=2.6, n_jobs=2)
    analyzer.compute("templates", operators=["average", "median", "std"])
    analyzer.compute("noise_levels")

    print(analyzer)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    compute_waveforms (workers: 2 processes fork):   0%|          | 0/10 [00:00<?, ?it/s]
    compute_waveforms (workers: 2 processes fork):  20%|██        | 2/10 [00:00<00:00, 16.70it/s]
    compute_waveforms (workers: 2 processes fork): 100%|██████████| 10/10 [00:00<00:00, 50.98it/s]
    compute_waveforms (workers: 2 processes fork): 100%|██████████| 10/10 [00:00<00:00, 45.33it/s]

    noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
    noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 311.69it/s]
    SortingAnalyzer: 4 channels - 10 units - 1 segments - memory - sparse - has recording
    Loaded 4 extensions: random_spikes, waveforms, templates, noise_levels




.. GENERATED FROM PYTHON SOURCE LINES 46-49

The :code:`spikeinterface.qualitymetrics` submodule has a set of functions that allow users to compute
metrics in a compact and easy way. To compute a single metric, one can simply run one of the
quality metric functions as shown below. Each function has a variety of adjustable parameters that can be tuned.

.. GENERATED FROM PYTHON SOURCE LINES 49-58

.. code-block:: Python


    presence_ratios = compute_presence_ratios(analyzer)
    print(presence_ratios)
    isi_violation_ratio, isi_violations_count = compute_isi_violations(analyzer)
    print(isi_violation_ratio)
    snrs = compute_snrs(analyzer)
    print(snrs)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    {np.str_('0'): nan, np.str_('1'): nan, np.str_('2'): nan, np.str_('3'): nan, np.str_('4'): nan, np.str_('5'): nan, np.str_('6'): nan, np.str_('7'): nan, np.str_('8'): nan, np.str_('9'): nan}
    {np.str_('0'): np.float64(0.0), np.str_('1'): np.float64(0.0), np.str_('2'): np.float64(0.0), np.str_('3'): np.float64(0.0), np.str_('4'): np.float64(0.0), np.str_('5'): np.float64(0.0), np.str_('6'): np.float64(0.0), np.str_('7'): np.float64(0.0), np.str_('8'): np.float64(0.0), np.str_('9'): np.float64(0.0)}
    {np.str_('0'): np.float64(10.592561908559091), np.str_('1'): np.float64(9.523682357288783), np.str_('2'): np.float64(20.169621243286517), np.str_('3'): np.float64(13.950197860914455), np.str_('4'): np.float64(15.910669823734091), np.str_('5'): np.float64(36.9911733515719), np.str_('6'): np.float64(47.09355132689412), np.str_('7'): np.float64(6.614492185397515), np.str_('8'): np.float64(9.99342258717766), np.str_('9'): np.float64(14.424255855502135)}




.. GENERATED FROM PYTHON SOURCE LINES 59-62

To compute more than one metric at once, we can use the :code:`SortingAnalyzer.compute("quality_metrics")`
function and indicate which metrics we want to compute. Then we can retrieve the results using the :code:`get_data()`
method as a ``pandas.DataFrame``.

.. GENERATED FROM PYTHON SOURCE LINES 62-73

.. code-block:: Python


    metrics_ext = analyzer.compute(
        "quality_metrics",
        metric_names=["presence_ratio", "snr", "amplitude_cutoff"],
        metric_params={
            "presence_ratio": {"bin_duration_s": 2.0},
        }
    )
    metrics = metrics_ext.get_data()
    print(metrics)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

       presence_ratio        snr
    0             1.0  10.592562
    1             1.0   9.523682
    2             1.0  20.169621
    3             1.0  13.950198
    4             1.0  15.910670
    5             1.0  36.991173
    6             1.0  47.093551
    7             1.0   6.614492
    8             1.0   9.993423
    9             1.0  14.424256




.. GENERATED FROM PYTHON SOURCE LINES 74-76

Some metrics are based on the principal component scores, so the extension
must be computed before. For instance:

.. GENERATED FROM PYTHON SOURCE LINES 76-88

.. code-block:: Python


    analyzer.compute("principal_components", n_components=3, mode="by_channel_global", whiten=True)

    metrics_ext = analyzer.compute(
        "quality_metrics",
        metric_names=[
            "mahalanobis",
            "d_prime",
        ],
    )
    metrics = metrics_ext.get_data()
    print(metrics)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Fitting PCA:   0%|          | 0/10 [00:00<?, ?it/s]
    Fitting PCA: 100%|██████████| 10/10 [00:00<00:00, 175.48it/s]

    Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
    Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 1827.74it/s]
       isolation_distance       l_ratio    d_prime        snr  presence_ratio
    0           22.198149  1.668703e-01   2.179627  10.592562             1.0
    1           18.095874  3.700155e-01   2.894958   9.523682             1.0
    2           27.954372  8.275506e-02   2.746692  20.169621             1.0
    3           80.142871  2.749780e-03   5.360497  13.950198             1.0
    4           45.146303  2.734921e-03   2.814640  15.910670             1.0
    5          138.949762  1.680033e-09   6.818582  36.991173             1.0
    6         1538.566181  1.742419e-07  11.335258  47.093551             1.0
    7           17.427044  3.718084e-01   1.276491   6.614492             1.0
    8           40.426621  5.238874e-03   4.558544   9.993423             1.0
    9           29.578126  4.374912e-02   2.706163  14.424256             1.0





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.456 seconds)


.. _sphx_glr_download_tutorials_metrics_plot_3_quality_metrics.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_3_quality_metrics.ipynb <plot_3_quality_metrics.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_3_quality_metrics.py <plot_3_quality_metrics.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_3_quality_metrics.zip <plot_3_quality_metrics.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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