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

.. only:: html

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

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

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

.. _sphx_glr_tutorials_comparison_generate_erroneous_sorting.py:


This generate a erroneous sorting to illustrate in some example
some possible mistake catch in ground truth comparison.

.. GENERATED FROM PYTHON SOURCE LINES 6-102

.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    import spikeinterface.extractors as se
    import spikeinterface.comparison as sc
    import spikeinterface.widgets as sw


    def generate_erroneous_sorting():
        """
        Generate an erroneous spike sorting for illustration purposes.

        This function creates a toy example recording and true sorting using the
        `toy_example` function. It then introduces
        various errors in the true sorting to create an erroneous sorting.

        The specific types of errors are:

        * Units 1 and 2 are perfect and have no errors.
        * Units 3, 4, and 10 have medium to low agreement with the true sorting.
        * Units 5 and 6 are over-merged, meaning that they contain spikes from multiple true units.
        * Unit 7 is over-split, meaning that it contains spikes from a true unit that has been split into two parts.
        * Unit 8 is redundant and appears 3 times in the erroneous sorting.
        * Unit 9 is completely missing from the erroneous sorting.
        * Units 15, 16, and 17 do not exist in the true sorting, but are included in the erroneous sorting.

        Returns:
            A tuple containing the true sorting and the erroneous sorting in that order
        """

        rec, sorting_true = se.toy_example(num_channels=4, num_units=10, duration=10, seed=10, num_segments=1)

        # artificially remap to one based
        sorting_true = sorting_true.select_units(unit_ids=None, renamed_unit_ids=np.arange(10, dtype="int64") + 1)

        sampling_frequency = sorting_true.get_sampling_frequency()

        units_err = {}

        # sorting_true have 10 units
        np.random.seed(0)

        # unit 1 2 are perfect
        for u in [1, 2]:
            st = sorting_true.get_unit_spike_train(u)
            units_err[u] = st

        # unit 3 4 (medium) 10 (low) have medium to low agreement
        for u, score in [(3, 0.8), (4, 0.75), (10, 0.3)]:
            st = sorting_true.get_unit_spike_train(u)
            st = np.sort(np.random.choice(st, size=int(st.size * score), replace=False))
            units_err[u] = st

        # unit 5 6 are over merge
        st5 = sorting_true.get_unit_spike_train(5)
        st6 = sorting_true.get_unit_spike_train(6)
        st = np.unique(np.concatenate([st5, st6]))
        st = np.sort(np.random.choice(st, size=int(st.size * 0.7), replace=False))
        units_err[56] = st

        # unit 7 is over split in 2 part
        st7 = sorting_true.get_unit_spike_train(7)
        st70 = st7[::2]
        units_err[70] = st70
        st71 = st7[1::2]
        st71 = np.sort(np.random.choice(st71, size=int(st71.size * 0.9), replace=False))
        units_err[71] = st71

        # unit 8 is redundant 3 times
        st8 = sorting_true.get_unit_spike_train(8)
        st80 = np.sort(np.random.choice(st8, size=int(st8.size * 0.65), replace=False))
        st81 = np.sort(np.random.choice(st8, size=int(st8.size * 0.6), replace=False))
        st82 = np.sort(np.random.choice(st8, size=int(st8.size * 0.55), replace=False))
        units_err[80] = st80
        units_err[81] = st81
        units_err[82] = st82

        # unit 9 is missing

        # there are some units that do not exist 15 16 and 17
        nframes = rec.get_num_frames(segment_index=0)
        for u in [15, 16, 17]:
            st = np.sort(np.random.randint(0, high=nframes, size=35))
            units_err[u] = st
        sorting_err = se.NumpySorting.from_unit_dict(units_err, sampling_frequency)

        return sorting_true, sorting_err


    if __name__ == "__main__":
        # just for check
        sorting_true, sorting_err = generate_erroneous_sorting()
        comp = sc.compare_sorter_to_ground_truth(sorting_true, sorting_err, exhaustive_gt=True)
        sw.plot_agreement_matrix(comp, ordered=True)
        plt.show()


.. _sphx_glr_download_tutorials_comparison_generate_erroneous_sorting.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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