.. _sphx_glr_auto_examples_plot_labels.py:


=============================
Explore labels
=============================

In this example, we load in a single subject example, load a model, and predict activity at all
model locations. We then plot locations, which are colored labels 'observed' and 'reconstructed'.





.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/images/sphx_glr_plot_labels_001.png
            :scale: 47

    *

      .. image:: /auto_examples/images/sphx_glr_plot_labels_002.png
            :scale: 47





.. code-block:: python


    # Code source: Lucy Owen & Andrew Heusser
    # License: MIT

    import supereeg as se

    # load example data
    bo = se.load('example_data')

    # plot original locations
    bo.plot_locs()

    # load example model
    model = se.load('example_model')

    # the default will replace the electrode location with the nearest voxel and reconstruct at all other locations
    reconstructed_bo = model.predict(bo, nearest_neighbor=False)

    # plot the all reconstructed locations
    reconstructed_bo.plot_locs()

**Total running time of the script:** ( 0 minutes  22.511 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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