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

.. only:: html

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

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

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

.. _sphx_glr__gallery_machine_learning_covariance.py:


Explore Covariance Matrix
=========================

Example showing how you can explore a covariance matrix with a selector tool.

.. GENERATED FROM PYTHON SOURCE LINES 7-94



.. image-sg:: /_gallery/machine_learning/images/sphx_glr_covariance_001.webp
   :alt: covariance
   :srcset: /_gallery/machine_learning/images/sphx_glr_covariance_001.webp
   :class: sphx-glr-single-img


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

 .. code-block:: none

    downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /home/runner/scikit_learn_data
    /home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/_features/_base.py:18: UserWarning: casting float64 array to float32
      warn(f"casting {array.dtype} array to float32")







|

.. code-block:: Python


    # test_example = false


    import fastplotlib as fpl
    from sklearn import datasets
    from sklearn.preprocessing import StandardScaler

    # load faces dataset
    faces = datasets.fetch_olivetti_faces()
    data = faces["data"]

    # sort the data so it's easier to understand the covariance matrix
    targets = faces["target"]
    sort_indices = targets.argsort()
    targets_sorted = targets[sort_indices]

    X = data[sort_indices]

    # scale the data w.r.t. mean and standard deviation
    X = StandardScaler().fit_transform(X)

    # compute covariance matrix
    X = X.T
    cov = X @ X.T / X.shape[1]

    # reshaped image for each sample wil be 64 x 64 pixels
    img = cov[0].reshape(64, 64)

    # figure kwargs for image widget
    # controller_ids = [[0, 1]] so we get independent controllers for each supblot
    # the covariance matrix is 4096 x 4096 and the reshaped image ix 64 x 64
    figure_kwargs = {"size": (700, 400), "controller_ids": [[0, 1]]}

    # create image widget
    iw = fpl.ImageWidget(
        data=[cov, img],  # display the covariance matrix and reshaped image of a row
        cmap="bwr",  # diverging colormap
        names=["covariance", "row image"],
        figure_kwargs=figure_kwargs,
    )

    # graphic that corresponds to image widget data array 0
    # 0 is the covariance matrix, 1 is the reshaped image of a row from the covariance matrix

    # add a linear selector to select y axis values so we can select rows of the cov matrix
    selector_cov = iw.managed_graphics[0].add_linear_selector(axis="y")

    # if you are exploring other types of matrices which are not-symmetric
    # you can also add a column selector by setting axis="x"

    # set vmin vmax
    for g in iw.managed_graphics:
        g.vmin, g.vmax = -1, 1


    # event handler when the covariance matrix row changes
    @selector_cov.add_event_handler("selection")
    def update_img(ev):
        # get the row index
        ix = ev.get_selected_index()

        # get the image the corresponds to this row
        img = cov[ix].reshape(64, 64)

        # change the reshaped image graphic data
        iw.managed_graphics[1].data = img


    figure = iw.figure  # not required, just for the docs gallery to pick it up


    # move the selector programmatically, this is mainly for the docs gallery
    # for real use you can interact with the selector with your mouse
    def animate():
        selector_cov.selection += 1


    iw.figure.add_animations(animate)

    iw.show()

    # NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
    # please see our docs for using fastplotlib interactively in ipython and jupyter
    if __name__ == "__main__":
        print(__doc__)
        fpl.run()


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

   **Total running time of the script:** (0 minutes 18.066 seconds)


.. _sphx_glr_download__gallery_machine_learning_covariance.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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