.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/qualitymetrics/plot_3_quality_mertics.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_mertics.py>`
        to download the full example code.

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

.. _sphx_glr_tutorials_qualitymetrics_plot_3_quality_mertics.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 9-21

.. code-block:: Python


    import spikeinterface.core as si
    import spikeinterface.extractors as se
    from spikeinterface.postprocessing import compute_principal_components
    from spikeinterface.qualitymetrics import (
        compute_snrs,
        compute_firing_rates,
        compute_isi_violations,
        calculate_pc_metrics,
        compute_quality_metrics,
    )








.. GENERATED FROM PYTHON SOURCE LINES 22-24

First, let's download a simulated dataset
from the repo 'https://gin.g-node.org/NeuralEnsemble/ephy_testing_data'

.. GENERATED FROM PYTHON SOURCE LINES 24-30

.. code-block:: Python


    local_path = si.download_dataset(remote_path="mearec/mearec_test_10s.h5")
    recording, sorting = se.read_mearec(local_path)
    print(recording)
    print(sorting)





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

 .. code-block:: none

    MEArecRecordingExtractor: 32 channels - 32.0kHz - 1 segments - 320,000 samples - 10.00s 
                              float32 dtype - 39.06 MiB
      file_path: /home/docs/spikeinterface_datasets/ephy_testing_data/mearec/mearec_test_10s.h5
    MEArecSortingExtractor: 10 units - 1 segments - 32.0kHz
      file_path: /home/docs/spikeinterface_datasets/ephy_testing_data/mearec/mearec_test_10s.h5




.. GENERATED FROM PYTHON SOURCE LINES 31-35

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

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

.. GENERATED FROM PYTHON SOURCE LINES 35-39

.. 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

    /home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/checkouts/3072/src/spikeinterface/core/job_tools.py:103: UserWarning: `n_jobs` is not set so parallel processing is disabled! To speed up computations, it is recommended to set n_jobs either globally (with the `spikeinterface.set_global_job_kwargs()` function) or locally (with the `n_jobs` argument). Use `spikeinterface.set_global_job_kwargs?` for more information about job_kwargs.
      warnings.warn(

    estimate_sparsity:   0%|          | 0/10 [00:00<?, ?it/s]
    estimate_sparsity: 100%|##########| 10/10 [00:00<00:00, 695.42it/s]
    SortingAnalyzer: 32 channels - 10 units - 1 segments - memory - sparse - has recording
    Loaded 0 extensions




.. GENERATED FROM PYTHON SOURCE LINES 40-42

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 42-51

.. 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:   0%|          | 0/10 [00:00<?, ?it/s]
    compute_waveforms:  10%|█         | 1/10 [00:00<00:01,  5.85it/s]
    compute_waveforms:  60%|██████    | 6/10 [00:00<00:00, 25.18it/s]
    compute_waveforms: 100%|██████████| 10/10 [00:00<00:00, 32.31it/s]

    noise_level:   0%|          | 0/20 [00:00<?, ?it/s]
    noise_level:  20%|##        | 4/20 [00:00<00:00, 38.78it/s]
    noise_level:  40%|####      | 8/20 [00:00<00:00, 38.89it/s]
    noise_level:  60%|######    | 12/20 [00:00<00:00, 39.21it/s]
    noise_level:  80%|########  | 16/20 [00:00<00:00, 39.28it/s]
    noise_level: 100%|##########| 20/20 [00:00<00:00, 39.27it/s]
    noise_level: 100%|##########| 20/20 [00:00<00:00, 39.16it/s]
    SortingAnalyzer: 32 channels - 10 units - 1 segments - memory - sparse - has recording
    Loaded 4 extensions: random_spikes, waveforms, templates, noise_levels




.. GENERATED FROM PYTHON SOURCE LINES 52-55

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 55-64

.. 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

    {'#0': 5.3, '#1': 5.0, '#2': 4.3, '#3': 3.0, '#4': 4.8, '#5': 3.7, '#6': 5.1, '#7': 11.1, '#8': 19.5, '#9': 12.9}
    {'#0': 0.0, '#1': 0.0, '#2': 0.0, '#3': 0.0, '#4': 0.0, '#5': 0.0, '#6': 0.0, '#7': 0.0, '#8': 0.0, '#9': 0.0}
    {'#0': 23.688985566223263, '#1': 25.562668727351635, '#2': 13.83221989734586, '#3': 21.930928721211213, '#4': 7.437540176388606, '#5': 7.451767727584651, '#6': 20.824094505903993, '#7': 7.341507851287954, '#8': 8.092695641281358, '#9': 8.95652685734799}




.. GENERATED FROM PYTHON SOURCE LINES 65-67

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 67-71

.. 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

    /home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/checkouts/3072/src/spikeinterface/qualitymetrics/misc_metrics.py:908: UserWarning: Some units have too few spikes : amplitude_cutoff is set to NaN
      warnings.warn(f"Some units have too few spikes : amplitude_cutoff is set to NaN")
        firing_rate        snr  amplitude_cutoff
    #0          5.3  23.688986              <NA>
    #1          5.0  25.562669              <NA>
    #2          4.3   13.83222              <NA>
    #3          3.0  21.930929              <NA>
    #4          4.8    7.43754              <NA>
    #5          3.7   7.451768              <NA>
    #6          5.1  20.824095              <NA>
    #7         11.1   7.341508              <NA>
    #8         19.5   8.092696              <NA>
    #9         12.9   8.956527              <NA>




.. GENERATED FROM PYTHON SOURCE LINES 72-74

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

.. GENERATED FROM PYTHON SOURCE LINES 74-85

.. 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

    /home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/checkouts/3072/src/spikeinterface/core/job_tools.py:103: UserWarning: `n_jobs` is not set so parallel processing is disabled! To speed up computations, it is recommended to set n_jobs either globally (with the `spikeinterface.set_global_job_kwargs()` function) or locally (with the `n_jobs` argument). Use `spikeinterface.set_global_job_kwargs?` for more information about job_kwargs.
      warnings.warn(

    Fitting PCA:   0%|          | 0/10 [00:00<?, ?it/s]
    Fitting PCA:  10%|█         | 1/10 [00:01<00:15,  1.75s/it]
    Fitting PCA:  40%|████      | 4/10 [00:02<00:02,  2.29it/s]
    Fitting PCA:  70%|███████   | 7/10 [00:02<00:00,  4.45it/s]
    Fitting PCA:  90%|█████████ | 9/10 [00:02<00:00,  4.09it/s]
    Fitting PCA: 100%|██████████| 10/10 [00:03<00:00,  3.96it/s]
    Fitting PCA: 100%|██████████| 10/10 [00:03<00:00,  3.21it/s]

    Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
    Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 151.98it/s]
    /home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/checkouts/3072/src/spikeinterface/core/job_tools.py:103: UserWarning: `n_jobs` is not set so parallel processing is disabled! To speed up computations, it is recommended to set n_jobs either globally (with the `spikeinterface.set_global_job_kwargs()` function) or locally (with the `n_jobs` argument). Use `spikeinterface.set_global_job_kwargs?` for more information about job_kwargs.
      warnings.warn(

    calculate pc_metrics:   0%|          | 0/10 [00:00<?, ?it/s]
    calculate pc_metrics:  10%|█         | 1/10 [00:00<00:01,  5.20it/s]
    calculate pc_metrics:  50%|█████     | 5/10 [00:00<00:00,  9.68it/s]
    calculate pc_metrics:  70%|███████   | 7/10 [00:00<00:00,  8.24it/s]
    calculate pc_metrics:  90%|█████████ | 9/10 [00:01<00:00,  7.48it/s]
    calculate pc_metrics: 100%|██████████| 10/10 [00:01<00:00,  8.40it/s]
           isolation_distance    d_prime        snr  amplitude_cutoff  firing_rate
    #0   111631078842116592.0  29.314857  23.688986              <NA>          5.3
    #1            22503.96533  27.426984  25.562669              <NA>          5.0
    #2   126067352822193792.0   24.75911   13.83222              <NA>          4.3
    #3   895298620985875968.0   30.90995  21.930929              <NA>          3.0
    #4   167417577384036896.0  28.448716    7.43754              <NA>          4.8
    #5    86612678156279200.0  20.730191   7.451768              <NA>          3.7
    #6  2142994535281708800.0  37.228947  20.824095              <NA>          5.1
    #7            8878.390636  28.968743   7.341508              <NA>         11.1
    #8              5827.4073  20.999115   8.092696              <NA>         19.5
    #9             7100.39125  30.249492   8.956527              <NA>         12.9





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

   **Total running time of the script:** (0 minutes 5.745 seconds)


.. _sphx_glr_download_tutorials_qualitymetrics_plot_3_quality_mertics.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_mertics.ipynb <plot_3_quality_mertics.ipynb>`

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

      :download:`Download Python source code: plot_3_quality_mertics.py <plot_3_quality_mertics.py>`

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

      :download:`Download zipped: plot_3_quality_mertics.zip <plot_3_quality_mertics.zip>`


.. only:: html

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

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