.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_06_manipulating_images_plot_smooth_mean_image.py>`     to download the full example code or to run this example in your browser via Binder
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_06_manipulating_images_plot_smooth_mean_image.py:


Smoothing an image
===================

Here we smooth a mean EPI image and plot the result

As we vary the smoothing FWHM, note how we decrease the amount of noise,
but also loose spatial details. In general, the best amount of smoothing
for a given analysis depends on the spatial extent of the effects that
are expected.



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_smooth_mean_image_001.png
          :alt: plot smooth mean image
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_smooth_mean_image_002.png
          :alt: plot smooth mean image
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_smooth_mean_image_003.png
          :alt: plot smooth mean image
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_smooth_mean_image_004.png
          :alt: plot smooth mean image
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_smooth_mean_image_005.png
          :alt: plot smooth mean image
          :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    First subject functional nifti image (4D) are located at: /home/varoquau/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
    /home/varoquau/dev/nilearn/nilearn/image/image.py:211: UserWarning: The parameter 'fwhm' for smoothing is specified as 0. Setting it to None (no smoothing will be performed)
      warnings.warn("The parameter 'fwhm' for smoothing is specified "






|


.. code-block:: default


    from nilearn import datasets, plotting, image

    data = datasets.fetch_development_fmri(n_subjects=1)

    # Print basic information on the dataset
    print('First subject functional nifti image (4D) are located at: %s' %
          data.func[0])

    first_epi_file = data.func[0]

    # First the compute the mean image, from the 4D series of image
    mean_func = image.mean_img(first_epi_file)

    # Then we smooth, with a varying amount of smoothing, from none to 20mm
    # by increments of 5mm
    for smoothing in range(0, 25, 5):
        smoothed_img = image.smooth_img(mean_func, smoothing)
        plotting.plot_epi(smoothed_img,
                          title="Smoothing %imm" % smoothing)


    plotting.show()


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

   **Total running time of the script:** ( 0 minutes  2.958 seconds)


.. _sphx_glr_download_auto_examples_06_manipulating_images_plot_smooth_mean_image.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/master?filepath=examples/auto_examples/06_manipulating_images/plot_smooth_mean_image.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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