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

.. only:: html

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

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

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

.. _sphx_glr_examples_make_graph_gaussian.py:

Make Gaussian Graph.
========================

This example demonstrates how to create a graph from EEG electrode positions using a gaussian
kernel. The example uses the Biosemi 64 channel montage. The graph is created with two different
values of sigma (0.1 and 0.3) and epsilon=0.5.

.. GENERATED FROM PYTHON SOURCE LINES 8-9

.. code-block:: Python

    import matplotlib.pyplot as plt







.. GENERATED FROM PYTHON SOURCE LINES 10-15

.. code-block:: Python

    import mne
    import numpy as np

    from eegrasp import EEGrasp








.. GENERATED FROM PYTHON SOURCE LINES 16-23

.. code-block:: Python


    montage = mne.channels.make_standard_montage('biosemi64')
    ch_names = montage.ch_names
    eeg_pos = montage.get_positions()['ch_pos']
    # Restructure into array
    eeg_pos = np.array([pos for _, pos in eeg_pos.items()])








.. GENERATED FROM PYTHON SOURCE LINES 24-33

.. code-block:: Python


    # Initialize EEGrasp object
    eegrasp = EEGrasp(coordinates=eeg_pos)
    # Compute distance matrix
    Z = eegrasp.compute_distance(method='Euclidean')
    # Compute weight matrix
    G = eegrasp.compute_graph(epsilon=0.5, sigma=0.1, distances=Z)
    W = eegrasp.graph_weights








.. GENERATED FROM PYTHON SOURCE LINES 34-39

.. code-block:: Python


    # Compute new weight matrix
    G2 = eegrasp.compute_graph(epsilon=0.5, sigma=0.3, distances=Z)
    W2 = eegrasp.graph_weights








.. GENERATED FROM PYTHON SOURCE LINES 40-69

.. code-block:: Python


    #  Plot Montage
    fig = plt.figure(figsize=(14, 8))
    ax = fig.add_subplot(1, 2, 1, projection='3d')

    montage.plot(kind='3d', axes=ax, show=False)
    ax.view_init(azim=70, elev=15)  # set view angle
    plt.title('Electrode Positions')

    # Plot Graph
    ax = fig.add_subplot(2, 2, 2)
    im = ax.imshow(W, cmap='viridis')
    ax.set_title('Weight Matrix (sigma=0.1, epsilon=0.5)')
    plt.colorbar(im, label='Weight')

    ax.set_xlabel('Electrode Index')
    ax.set_ylabel('Electrode Index')

    # Plot new Graph
    ax = fig.add_subplot(2, 2, 4)
    im = ax.imshow(W2, cmap='viridis')
    ax.set_title('Weight Matrix (sigma=0.3, epsilon=0.5)')
    plt.colorbar(im, label='Weight')

    ax.set_xlabel('Electrode Index')
    ax.set_ylabel('Electrode Index')

    plt.tight_layout()
    plt.show()



.. image-sg:: /examples/images/sphx_glr_make_graph_gaussian_001.png
   :alt: Electrode Positions, Weight Matrix (sigma=0.1, epsilon=0.5), Weight Matrix (sigma=0.3, epsilon=0.5)
   :srcset: /examples/images/sphx_glr_make_graph_gaussian_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.788 seconds)

**Estimated memory usage:**  189 MB


.. _sphx_glr_download_examples_make_graph_gaussian.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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