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

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

.. _sphx_glr_auto_examples_plot_simulate_data.py:


=============================
Simulate subject data
=============================

In this example, we simulate data





.. image:: /auto_examples/images/sphx_glr_plot_simulate_data_001.png
    :class: sphx-glr-single-img





.. code-block:: python

    # Code source: Lucy Owen
    # License: MIT

    # load timecorr
    import timecorr as tc
    import seaborn as sns
    import matplotlib.pyplot as plt

    # simulate some data
    data, corrs = tc.simulate_data(datagen='block', return_corrs=True, set_random_seed=True, S=1, T=100, K=10, B=5)

    # calculate correlations  - returned squareformed
    tc_vec_data = tc.timecorr(tc.simulate_data(), weights_function=tc.gaussian_weights, weights_params={'var': 5}, combine=tc.helpers.corrmean_combine)

    # convert from vector to matrix format
    tc_mat_data = tc.vec2mat(tc_vec_data)

    # plot the 3 correlation matrices different timepoints

    sns.heatmap(tc_mat_data[:, :, 48])
    plt.show()
    plt.clf()
    sns.heatmap(tc_mat_data[:, :, 50])
    plt.show()
    plt.clf()
    sns.heatmap(tc_mat_data[:, :, 52])
    plt.show()
    plt.clf()


**Total running time of the script:** ( 0 minutes  0.423 seconds)


.. _sphx_glr_download_auto_examples_plot_simulate_data.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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