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

.. only:: html

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

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

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

.. _sphx_glr_examples_pattern_processing_neighbour_pattern_averaging.py:


===========================
Neighbour pattern averaging
===========================

This example shows how to average each pattern in a scan with its nearest neighbours
using :meth:`~kikuchipy.signals.EBSD.average_neighbour_patterns`.

More details are given in the
:doc:`pattern processing tutorial </tutorials/pattern_processing>` and the
:doc:`feature maps tutorial </tutorials/feature_maps>`.

.. GENERATED FROM PYTHON SOURCE LINES 13-55



.. image-sg:: /examples/pattern_processing/images/sphx_glr_neighbour_pattern_averaging_001.png
   :alt: Static + Dynamic, Static + Dynamic + Averaging
   :srcset: /examples/pattern_processing/images/sphx_glr_neighbour_pattern_averaging_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    import hyperspy.api as hs
    import kikuchipy as kp
    import matplotlib.pyplot as plt


    # Silence progressbars
    hs.preferences.General.show_progressbar = False

    # Load Ni patterns and subtract static and dynamic background
    s = kp.data.nickel_ebsd_large()
    s.remove_static_background()
    s.remove_dynamic_background()

    # Get image quality before averaging
    iq0 = s.get_image_quality()

    # Keep one pattern for comparison
    x, y = (50, 8)
    pattern0 = s.inav[x, y].deepcopy()

    # Average in a (3, 3) window with a Gaussian kernel with a standard
    # deviation of 1
    s.average_neighbour_patterns(window="gaussian", std=1)

    iq1 = s.get_image_quality()
    pattern1 = s.inav[x, y]

    # Plot pattern and histograms of image qualities before and after
    # averaging
    fig, axes = plt.subplots(2, 2, height_ratios=[3, 1.5])
    for ax, pattern, title in zip(
        axes[0],
        [pattern0, pattern1],
        ["Static + Dynamic", "Static + Dynamic + Averaging"],
    ):
        ax.imshow(pattern, cmap="gray")
        ax.set_title(title)
        ax.axis("off")
    for ax, iq in zip(axes[1], [iq0, iq1]):
        ax.hist(iq.ravel(), bins=100)
    fig.tight_layout()


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

   **Total running time of the script:** (0 minutes 5.180 seconds)

**Estimated memory usage:**  120 MB


.. _sphx_glr_download_examples_pattern_processing_neighbour_pattern_averaging.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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