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

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

.. _sphx_glr_gallery_neuropixel.py:


natural scene decoding from V1
==============================

This is an example of how to decode natural images from spikes recorded in V1




.. code-block:: python


    from __future__ import print_function


first, we need to load the data



.. code-block:: python


    data_path = '/allen/aibs/mat/RamIyer/frm_Dan/NWBFilesSev/V1_NI_pkl_data/'

    import pandas as pd
    ephys_data = pd.read_pickle(data_path+'M15_ni_data.pkl')


Let's get the dataframe of image presentations and rename the columns



.. code-block:: python


    events = ephys_data['stim_table'].rename(
        columns={
            'Start':'time',
            'Frame':'image_id',
        },
    )
    print(events.head())


Next, let's reformat the spike times into a single table



.. code-block:: python



    from neuroglia.nwb import SpikeTablizer
    spikes = SpikeTablizer().fit_transform(ephys_data['spiketimes'])
    print(spikes.head())


Now, we'll sample spikes near each event & build this into a xarray 3D tensor



.. code-block:: python


    from neuroglia.event import PeriEventSpikeSampler
    import numpy as np
    spike_sampler = PeriEventSpikeSampler(
        spikes=spikes,
        sample_times=np.arange(0.1,0.35,0.01),
    )
    tensor = spike_sampler.fit_transform(events)
    print(tensor)


We can get the average elicited spike count with the `ResponseReducer`



.. code-block:: python


    from neuroglia.tensor import ResponseReducer
    import numpy as np
    reducer = ResponseReducer(func=np.mean)
    means = reducer.fit_transform(tensor)
    print(means)


Let's use the scikit-learn pipeline to chain these steps into a single
decoding pipeline



.. code-block:: python


    from sklearn.pipeline import Pipeline
    from sklearn.neighbors import KNeighborsClassifier

    pipeline = Pipeline([
        ('spike_sampler',PeriEventSpikeSampler(spikes=spikes,sample_times=np.arange(0.1,0.35,0.01))),
        ('extract', ResponseReducer(func=np.mean)),
        ('classify', KNeighborsClassifier()),
    ])


Now we can train the full pipeline on the training set



.. code-block:: python


    from sklearn.model_selection import train_test_split

    X_train, X_test, y_train, y_test = train_test_split(
        events, events['image_id'],test_size=0.33,
    )

    pipeline.fit(X_train,y_train)


Finally we'll test the pipeline on the held out data



.. code-block:: python


    score = pipeline.score(X_test,y_test)

    n_images = len(events['image_id'].unique())
    print(score*n_images)


.. _sphx_glr_download_gallery_neuropixel.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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