.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/00_encodingdecoding/fit_residuals.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_00_encodingdecoding_fit_residuals.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_00_encodingdecoding_fit_residuals.py:


Fit the residual covariance matrix
=============================================

In this example, we fit a residual noise covariance matrix to
simulated data from a ``VonMisesPRF``-model.

.. GENERATED FROM PYTHON SOURCE LINES 9-40

.. code-block:: Python


    # We set up a simple VonMisesPRF model
    from braincoder.models import VonMisesPRF
    import numpy as np
    import pandas as pd

    # Set up six evenly spaced von Mises PRFs
    centers = np.linspace(0.0, 2*np.pi, 6, endpoint=False)
    parameters = pd.DataFrame({'mu':centers, 'kappa':1., 'amplitude':1.0, 'baseline':0.0},
                              index=pd.Index([f'Voxel {i+1}' for i in range(6)], name='voxel')).astype(np.float32)

    # We have only 3 voxels, each with a linear combination of the 6 von Mises functions:
    weights = np.array([[1, 0, 1],
                        [1, .5, 1],
                        [0, 1, 0],
                        [0, .5, 0],
                        [0, 0, 1],
                        [0, 0, 1]]).astype(np.float32)

    model = VonMisesPRF(parameters=parameters, weights=weights)

    # 50 random orientations
    paradigm = np.random.rand(50) * np.pi*2

    # Arbitrary covariance matrix
    cov = np.array([[.5, 0.0, 0.0],
           [.25, .75, .25],
           [.25, .25, .75]])

    data = model.simulate(noise=cov, weights=weights, paradigm=paradigm)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /Users/gdehol/mambaforge/lib/python3.10/site-packages/scipy/stats/_multivariate.py:758: RuntimeWarning: covariance is not symmetric positive-semidefinite.
      out = random_state.multivariate_normal(mean, cov, size)




.. GENERATED FROM PYTHON SOURCE LINES 41-53

.. code-block:: Python


    # Import ResidualFitter
    from braincoder.optimize import ResidualFitter

    fitter = ResidualFitter(model, data, paradigm, parameters, weights)

    # omega is the covariance matrix, dof can be estimated when a 
    # multivariate t-distribution (rather than a normal distribution)
    # is used
    omega, dof = fitter.fit(progressbar=False)
    print(omega)

    # %%



.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    init_tau: 0.5883835554122925, 0.9530800580978394
    WWT max: 4.0
    [[0.3484391  0.05987196 0.07491002]
     [0.05987195 0.60535425 0.09866075]
     [0.07491002 0.09866075 0.9463547 ]]





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

   **Total running time of the script:** (0 minutes 1.791 seconds)


.. _sphx_glr_download_auto_examples_00_encodingdecoding_fit_residuals.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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