.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/qualitymetrics/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_qualitymetrics_plot_3_quality_metrics.py>`
        to download the full example code.

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

.. _sphx_glr_tutorials_qualitymetrics_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 9-18

.. code-block:: Python


    import spikeinterface.core as si
    from spikeinterface.qualitymetrics import (
        compute_snrs,
        compute_firing_rates,
        compute_isi_violations,
        compute_quality_metrics,
    )








.. GENERATED FROM PYTHON SOURCE LINES 19-20

First, let's generate a simulated recording and sorting

.. GENERATED FROM PYTHON SOURCE LINES 20-25

.. 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 26-30

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

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

.. GENERATED FROM PYTHON SOURCE LINES 30-34

.. 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


    estimate_sparsity (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
    estimate_sparsity (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 464.78it/s]
    SortingAnalyzer: 4 channels - 10 units - 1 segments - memory - sparse - has recording
    Loaded 0 extensions




.. GENERATED FROM PYTHON SOURCE LINES 35-37

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 37-46

.. 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):   0%|          | 0/10 [00:00<?, ?it/s]
    compute_waveforms (workers: 2 processes):  80%|████████  | 8/10 [00:00<00:00, 73.71it/s]
    compute_waveforms (workers: 2 processes): 100%|██████████| 10/10 [00:00<00:00, 86.98it/s]

    noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
    noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 279.05it/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 47-50

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 50-59

.. code-block:: Python


    firing_rates = compute_firing_rates(analyzer)
    print(firing_rates)
    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'): 15.9, np.str_('1'): 17.4, np.str_('2'): 14.3, np.str_('3'): 15.4, np.str_('4'): 15.6, np.str_('5'): 16.3, np.str_('6'): 14.6, np.str_('7'): 15.2, np.str_('8'): 16.3, np.str_('9'): 15.7}
    {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(0.8115581261096024), np.str_('1'): np.float64(12.148417738280905), np.str_('2'): np.float64(2.937305291301113), np.str_('3'): np.float64(15.222005999580237), np.str_('4'): np.float64(4.905677941163871), np.str_('5'): np.float64(22.62981036736856), np.str_('6'): np.float64(34.107214182773816), np.str_('7'): np.float64(2.000744910270261), np.str_('8'): np.float64(13.100944551144812), np.str_('9'): np.float64(26.655169806779853)}




.. GENERATED FROM PYTHON SOURCE LINES 60-62

To compute more than one metric at once, we can use the :code:`compute_quality_metrics` function and indicate
which metrics we want to compute. This will return a pandas dataframe:

.. GENERATED FROM PYTHON SOURCE LINES 62-66

.. code-block:: Python


    metrics = compute_quality_metrics(analyzer, metric_names=["firing_rate", "snr", "amplitude_cutoff"])
    print(metrics)





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

 .. code-block:: none

       firing_rate        snr  amplitude_cutoff
    0         15.9   0.811558               NaN
    1         17.4  12.148418               NaN
    2         14.3   2.937305               NaN
    3         15.4  15.222006               NaN
    4         15.6   4.905678               NaN
    5         16.3  22.629810               NaN
    6         14.6  34.107214               NaN
    7         15.2   2.000745               NaN
    8         16.3  13.100945               NaN
    9         15.7  26.655170               NaN




.. GENERATED FROM PYTHON SOURCE LINES 67-69

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

.. GENERATED FROM PYTHON SOURCE LINES 69-80

.. code-block:: Python


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

    metrics = compute_quality_metrics(
        analyzer,
        metric_names=[
            "isolation_distance",
            "d_prime",
        ],
    )
    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, 177.31it/s]

    Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
    Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 2388.83it/s]

    calculate pc_metrics:   0%|          | 0/10 [00:00<?, ?it/s]
    calculate pc_metrics: 100%|██████████| 10/10 [00:00<00:00, 531.37it/s]
       isolation_distance    d_prime  firing_rate  amplitude_cutoff        snr
    0            5.732070   1.575294         15.9               NaN   0.811558
    1           64.558223   3.444697         17.4               NaN  12.148418
    2           13.797184   1.327231         14.3               NaN   2.937305
    3          108.959216   5.329499         15.4               NaN  15.222006
    4           34.651664   3.085616         15.6               NaN   4.905678
    5          332.565139   8.813182         16.3               NaN  22.629810
    6          718.273351  12.875703         14.6               NaN  34.107214
    7            5.261477   1.371375         15.2               NaN   2.000745
    8          168.832398   4.488255         16.3               NaN  13.100945
    9         1225.517084  11.425180         15.7               NaN  26.655170





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

   **Total running time of the script:** (0 minutes 0.371 seconds)


.. _sphx_glr_download_tutorials_qualitymetrics_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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_