.. _sphx_glr_auto_examples_plot_simulate_mo.py:


=============================
Simulate model object
=============================

In this example, we simulate 3 brain objects using a subset of 10
locations. We will impose a correlational structure (a toeplitz matrix) on
our simulated brain objects.  Then, we will create a model from these brain
objects and plot it.





.. image:: /auto_examples/images/sphx_glr_plot_simulate_mo_001.png
    :align: center





.. code-block:: python


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

    import supereeg as se

    # simulate 100 locations
    locs = se.simulate_locations(n_elecs=100)

    # simulate correlation matrix
    R = se.create_cov(cov='toeplitz', n_elecs=len(locs))

    # create list of simulated brain objects
    model_bos = [se.simulate_model_bos(n_samples=1000, sample_rate=1000, cov=R,
                                       locs=locs, sample_locs=10) for x in range(3)]

    # create model from subsampled gray locations
    model = se.Model(model_bos, locs=locs)

    # plot the model
    model.plot_data()

**Total running time of the script:** ( 0 minutes  2.548 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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