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

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

.. _sphx_glr_auto_examples_calculate_dynamic_correlations.py:


=============================
Calculate dynamic correlations
=============================

In this example, we calculate dynamic correlations


.. code-block:: default

    # Code source: Lucy Owen
    # License: MIT

    # load timecorr and other packages
    import timecorr as tc
    import numpy as np


    S = 1
    T = 1000
    K = 10
    B = 5

    # define your weights parameters
    width = 100
    laplace = {'name': 'Laplace', 'weights': tc.laplace_weights, 'params': {'scale': width}}

    # calculate the dynamic correlation of the two datasets

    subs_data_2 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=1, S=S, T=T, K=K, B=B)

    subs_data_1 = tc.simulate_data(datagen='ramping', return_corrs=False, set_random_seed=2, S=S, T=T, K=K, B=B)



    wcorred_data = tc.wcorr(np.array(subs_data_1),  np.array(subs_data_2), weights=laplace['weights'](T))

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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_calculate_dynamic_correlations.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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