.. 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 9-21

.. 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


    estimate_sparsity (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
    estimate_sparsity (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 1298.39it/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 (workers: 2 processes):   0%|          | 0/10 [00:00<?, ?it/s]
    compute_waveforms (workers: 2 processes):  50%|█████     | 5/10 [00:00<00:00, 42.85it/s]
    compute_waveforms (workers: 2 processes): 100%|██████████| 10/10 [00:00<00:00, 85.47it/s]

    noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
    noise_level (no parallelization):  40%|████      | 8/20 [00:00<00:00, 73.88it/s]
    noise_level (no parallelization):  80%|████████  | 16/20 [00:00<00:00, 73.63it/s]
    noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 73.49it/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': 23.616042642620027, '#1': 25.702405907878106, '#2': 13.875965052750985, '#3': 21.94117241543328, '#4': 7.441164986487991, '#5': 7.451232277180385, '#6': 20.909913146091146, '#7': 7.375896010469314, '#8': 8.08395120217932, '#9': 8.959683964813891}




.. 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

        firing_rate        snr  amplitude_cutoff
    #0          5.3  23.616043               NaN
    #1          5.0  25.702406               NaN
    #2          4.3  13.875965               NaN
    #3          3.0  21.941172               NaN
    #4          4.8   7.441165               NaN
    #5          3.7   7.451232               NaN
    #6          5.1  20.909913               NaN
    #7         11.1   7.375896               NaN
    #8         19.5   8.083951               NaN
    #9         12.9   8.959684               NaN




.. 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


    Fitting PCA:   0%|          | 0/10 [00:00<?, ?it/s]
    Fitting PCA:  90%|█████████ | 9/10 [00:00<00:00, 77.71it/s]
    Fitting PCA: 100%|██████████| 10/10 [00:00<00:00, 71.42it/s]

    Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
    Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 492.08it/s]

    calculate pc_metrics:   0%|          | 0/10 [00:00<?, ?it/s]
    calculate pc_metrics:  50%|█████     | 5/10 [00:00<00:00, 49.61it/s]
    calculate pc_metrics: 100%|██████████| 10/10 [00:00<00:00, 85.03it/s]
        isolation_distance    d_prime  amplitude_cutoff  firing_rate        snr
    #0        1.116311e+17  29.314861               NaN          5.3  23.616043
    #1        2.250397e+04  27.426984               NaN          5.0  25.702406
    #2        1.260674e+17  24.759110               NaN          4.3  13.875965
    #3        8.952986e+17  30.909950               NaN          3.0  21.941172
    #4        1.674176e+17  28.448716               NaN          4.8   7.441165
    #5        8.661268e+16  20.730191               NaN          3.7   7.451232
    #6        2.142995e+18  37.228947               NaN          5.1  20.909913
    #7        8.878391e+03  28.968743               NaN         11.1   7.375896
    #8        5.827407e+03  20.999116               NaN         19.5   8.083951
    #9        7.100391e+03  30.249492               NaN         12.9   8.959684





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

   **Total running time of the script:** (0 minutes 0.937 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>`_