.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_nilearn_101.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_plot_nilearn_101.py:


Basic nilearn example: manipulating and looking at data
=======================================================

A simple example showing how to load an existing Nifti file and use
basic nilearn functionalities.


.. code-block:: default


    # Let us use a Nifti file that is shipped with nilearn
    from nilearn.datasets import MNI152_FILE_PATH

    # Note that the variable MNI152_FILE_PATH is just a path to a Nifti file
    print('Path to MNI152 template: %r' % MNI152_FILE_PATH)





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

 Out:

 .. code-block:: none

    Path to MNI152 template: '/home/varoquau/dev/nilearn/nilearn/datasets/data/avg152T1_brain.nii.gz'




A first step: looking at our data
----------------------------------

Let's quickly plot this file:


.. code-block:: default

    from nilearn import plotting
    plotting.plot_img(MNI152_FILE_PATH)




.. image:: /auto_examples/images/sphx_glr_plot_nilearn_101_001.png
    :alt: plot nilearn 101
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <nilearn.plotting.displays.OrthoSlicer object at 0x7f8bfc7ac700>



This is not a very pretty plot. We just used the simplest possible
code. There is a whole :ref:`section of the documentation <plotting>`
on making prettier code.

**Exercise**: Try plotting one of your own files. In the above,
MNI152_FILE_PATH is nothing more than a string with a path pointing to
a nifti image. You can replace it with a string pointing to a file on
your disk. Note that it should be a 3D volume, and not a 4D volume.

Simple image manipulation: smoothing
-------------------------------------

Let's use an image-smoothing function from nilearn:
:func:`nilearn.image.smooth_img`

Functions containing 'img' can take either a filename or an image as input.

Here we give as inputs the image filename and the smoothing value in mm


.. code-block:: default

    from nilearn import image
    smooth_anat_img = image.smooth_img(MNI152_FILE_PATH, fwhm=3)

    # While we are giving a file name as input, the function returns
    # an in-memory object:
    smooth_anat_img





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

 Out:

 .. code-block:: none


    <nibabel.nifti1.Nifti1Image object at 0x7f8c0e747730>



This is an in-memory object. We can pass it to nilearn function, for
instance to look at it


.. code-block:: default

    plotting.plot_img(smooth_anat_img)




.. image:: /auto_examples/images/sphx_glr_plot_nilearn_101_002.png
    :alt: plot nilearn 101
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <nilearn.plotting.displays.OrthoSlicer object at 0x7f8bfc73dc40>



We could also pass it to the smoothing function


.. code-block:: default

    more_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)
    plotting.plot_img(more_smooth_anat_img)




.. image:: /auto_examples/images/sphx_glr_plot_nilearn_101_003.png
    :alt: plot nilearn 101
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <nilearn.plotting.displays.OrthoSlicer object at 0x7f8bfc6f6f70>



Saving results to a file
-------------------------

We can save any in-memory object as follows:


.. code-block:: default

    more_smooth_anat_img.to_filename('more_smooth_anat_img.nii.gz')








Finally, calling plotting.show() is necessary to display the figure
when running as a script outside IPython


.. code-block:: default

    plotting.show()








|

______

To recap, all the nilearn tools can take data as filenames or in-memory
objects, and return brain volumes as in-memory objects. These can be
passed on to other nilearn tools, or saved to disk.


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

   **Total running time of the script:** ( 0 minutes  2.948 seconds)


.. _sphx_glr_download_auto_examples_plot_nilearn_101.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/plot_nilearn_101.ipynb
      :width: 150 px


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

     :download:`Download Python source code: plot_nilearn_101.py <plot_nilearn_101.py>`



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

     :download:`Download Jupyter notebook: plot_nilearn_101.ipynb <plot_nilearn_101.ipynb>`


.. only:: html

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

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