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

    Click :ref:`here <sphx_glr_download_auto_examples_NeoAll_examples_plot_NeoAll_ex4.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_NeoAll_examples_plot_NeoAll_ex4.py:


===========================
    Neo All - example 4
===========================

In this example, we look at the single unit activity around interictal epileptic discharges (IEDs). To do this we will
add events and define epochs to the NeoAll instance.




.. code-block:: python


    from neoStructures import *
    import pandas as pd
    import matplotlib.pyplot as plt
    from os.path import isdir, join, isfile
    import seaborn as sns
    import pickle
    sns.set()
    sns.set_context('paper')







In this example we will load the NeoAll instance from a pickle file



.. code-block:: python

    data_dir = join('pySpikeAnalysis', 'sample_data') if isdir('pySpikeAnalysis') else join('..', '..', 'pySpikeAnalysis', 'sample_data')
    neo_all_filename = r'neoall_071118_1132.p'
    if not isfile(join(data_dir, neo_all_filename)):
        raise ValueError('Cannot find the file {}'.format(join(data_dir, neo_all_filename)))
    with open(join(data_dir, neo_all_filename), 'rb') as f:
        neoAll = pickle.load(f)







See information about NeoAll :



.. code-block:: python

    print(neoAll)





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

 Out:

 .. code-block:: none

    NeoAll Instance with 19 units. 1 Neo segment per unit. Each segment contains 1 Neo spiketrain
    17 channel indexes
    49 channels and 49 electrodes


Before creating epochs, we first need to create events. They can represents stimuli onset or epileptic events for
instance. In this example, each event correponds to an Interictal Epileptic Discharge. There are two ways to create
events, either by providing the event_filepath parameter when instanciating a NeoAll instance or by calling the
:func:`neoStructures.NeoAll.read_event_file` method. The indexing of the csv file (i.e. to which field correspond each
column) is done in the **neoStructures_params.py** file.
Here we can see that the neoAll already contains events :



.. code-block:: python

    print(neoAll.segments[0].events)





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

 Out:

 .. code-block:: none

    [<Event: >]


We can access the times of the events and the name of the events :



.. code-block:: python

    print('Event name : {}'.format(neoAll.segments[0].events[0].name))
    print('Event times : {}'.format(neoAll.segments[0].events[0].times))





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

 Out:

 .. code-block:: none

    Event name : Pointe
    Event times : [ 127.59    132.4795  134.4737  149.087   230.2434  232.3191  270.5974
      441.8186  501.2786  617.0066  753.7445  770.4778  816.693   974.7162
      982.7807 1157.6631 1169.6899 1226.8002 1478.6519 1597.9215 1635.7244
     1904.1508 1912.3301 1937.7698 1963.7687 1998.6647 2029.0295 2105.2268
     2109.5313 2136.0479 2188.8211 2296.0886 2621.6179 2900.5309 3206.0556
     3321.9253 3387.3586 3627.9234 3790.2395 3927.836  4266.9786 4441.5314
     4585.17   4827.7493 5235.3367 5362.5499 5547.8424 5567.3848 5590.6934
     5674.0067 5791.6717] s


Let's now define epochs on each segments (NeoAll class contains one Neo.segment for each unit), using the
:func:`neoStructures.NeoAll.create_epochs_around_events`
We have to specify the epoch onset and duration (s), as well as the epochs name.



.. code-block:: python

    epoch_t_start = np.array([-0.500, -0.250, -0.050, 0.050, 0.250])
    epoch_duration = np.array([0.250, 0.200, 0.100, 0.200, 0.250])
    epoch_names = ['Pre-IED baseline', 'Pre-IED', 'IED', 'Slow wave', 'Post-IED']







We must specify around which event we want to create epochs



.. code-block:: python

    neoAll.create_epochs_around_events('Pointe', time_offset=epoch_t_start, epoch_duration=epoch_duration,
                                       epoch_names=epoch_names)







We can now study the behaviour of neurons on the different periods
of the IEDs we have defined.



.. code-block:: python

    neoAll.plot_spikerate_change_ratio_on_epochs('IED', 'Pre-IED')




.. image:: /auto_examples/NeoAll_examples/images/sphx_glr_plot_NeoAll_ex4_001.png
    :class: sphx-glr-single-img




We can see that some units start firing during the IED and are almost silent before, in the Pre-IED period. Further
analyses can be conducted using the NeoEpoch class which allows to plot rasterplot of the different units around
the events of interest.


**Total running time of the script:** ( 0 minutes  0.523 seconds)


.. _sphx_glr_download_auto_examples_NeoAll_examples_plot_NeoAll_ex4.py:


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 .. container:: sphx-glr-footer
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     :download:`Download Python source code: plot_NeoAll_ex4.py <plot_NeoAll_ex4.py>`



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