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

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

.. _sphx_glr_gallery_plot_bonc_natural_images.py:


natural scene decoding ophys
==============================

This is an example of how to decode natural images from ophys traces in V1



First, let's download an experiment from the Allen Institute Brain Observatory



.. code-block:: python

    from allensdk.core.brain_observatory_cache import BrainObservatoryCache

    boc = BrainObservatoryCache()

    OEID = 541206592
    nwb_dataset = boc.get_ophys_experiment_data(OEID)







Next, we'll load the dF/F traces and put them in a DataFrame



.. code-block:: python



    timestamps, dff = nwb_dataset.get_dff_traces()
    neuron_ids = nwb_dataset.get_cell_specimen_ids()

    import pandas as pd
    traces = pd.DataFrame(
        dff.T,
        columns=neuron_ids,
        index=timestamps,
    )

    print(traces.head())





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

 Out:

 .. code-block:: none

    541510267  541510270    ...      541509957  541511118
    10.30338   0.219740   0.151908    ...       0.360267   0.156850
    10.33655   0.167939   0.142997    ...       0.308192   0.108658
    10.36972   0.136697   0.068048    ...       0.295819   0.033781
    10.40289   0.157216   0.105795    ...       0.329818   0.098048
    10.43606   0.130490   0.097038    ...       0.487283   0.066526

    [5 rows x 154 columns]


Next, we'll load stim_table



.. code-block:: python


    stim_table = nwb_dataset.get_stimulus_table('natural_scenes')
    print(stim_table.head())





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

 Out:

 .. code-block:: none

    frame  start    end
    0     92  16126  16133
    1     27  16134  16141
    2     52  16141  16148
    3     37  16149  16156
    4    103  16156  16163


The stim_table lists stimulus times in terms of the start and end frames of
the calcium traces, but we need start times and durations for neuroglia, so
we'll need to reshape



.. code-block:: python


    stim_table['time'] = timestamps[stim_table['start']]
    stim_table['duration'] = timestamps[stim_table['end']+1] - stim_table['time']

    print(stim_table.head())





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

 Out:

 .. code-block:: none

    frame  start    end       time  duration
    0     92  16126  16133  545.22975   0.26537
    1     27  16134  16141  545.49512   0.26538
    2     52  16141  16148  545.72733   0.26537
    3     37  16149  16156  545.99270   0.26538
    4    103  16156  16163  546.22491   0.26538


Reduce the traces to responses



.. code-block:: python


    import numpy as np
    from neuroglia.epoch import EpochTraceReducer

    reducer = EpochTraceReducer(traces,func=np.mean)
    X = reducer.fit_transform(stim_table)
    y = stim_table['frame'].values







Do some dimensionality reduction on the responses



.. code-block:: python


    from sklearn.decomposition import PCA
    pca = PCA()
    X_reduced = pca.fit_transform(X)








Plot the first two Principal Components



.. code-block:: python


    import matplotlib.pyplot as plt
    plt.scatter(X_reduced[:,0],X_reduced[:,1],c=y)



.. image:: /gallery/images/sphx_glr_plot_bonc_natural_images_001.png
    :class: sphx-glr-single-img




**Total running time of the script:** ( 8 minutes  35.657 seconds)


.. _sphx_glr_download_gallery_plot_bonc_natural_images.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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