.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_nilearn_101.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. 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.
.. GENERATED FROM PYTHON SOURCE LINES 8-15
.. 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/nicolas/GitRepos/nilearn-fork/nilearn/datasets/data/mni_icbm152_t1_tal_nlin_sym_09a_converted.nii.gz'
.. GENERATED FROM PYTHON SOURCE LINES 16-20
A first step: looking at our data
----------------------------------
Let's quickly plot this file:
.. GENERATED FROM PYTHON SOURCE LINES 20-23
.. code-block:: default
from nilearn import plotting
plotting.plot_img(MNI152_FILE_PATH)
.. image-sg:: /auto_examples/images/sphx_glr_plot_nilearn_101_001.png
:alt: plot nilearn 101
:srcset: /auto_examples/images/sphx_glr_plot_nilearn_101_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fa540cf2ac0>
.. GENERATED FROM PYTHON SOURCE LINES 24-32
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.
.. GENERATED FROM PYTHON SOURCE LINES 34-43
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
.. GENERATED FROM PYTHON SOURCE LINES 43-50
.. 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 0x7fa540be1e50>
.. GENERATED FROM PYTHON SOURCE LINES 51-53
This is an in-memory object. We can pass it to nilearn function, for
instance to look at it
.. GENERATED FROM PYTHON SOURCE LINES 53-55
.. code-block:: default
plotting.plot_img(smooth_anat_img)
.. image-sg:: /auto_examples/images/sphx_glr_plot_nilearn_101_002.png
:alt: plot nilearn 101
:srcset: /auto_examples/images/sphx_glr_plot_nilearn_101_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fa540beb8e0>
.. GENERATED FROM PYTHON SOURCE LINES 56-57
We could also pass it to the smoothing function
.. GENERATED FROM PYTHON SOURCE LINES 57-60
.. code-block:: default
more_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)
plotting.plot_img(more_smooth_anat_img)
.. image-sg:: /auto_examples/images/sphx_glr_plot_nilearn_101_003.png
:alt: plot nilearn 101
:srcset: /auto_examples/images/sphx_glr_plot_nilearn_101_003.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fa52d4e2970>
.. GENERATED FROM PYTHON SOURCE LINES 61-65
Saving results to a file
-------------------------
We can save any in-memory object as follows:
.. GENERATED FROM PYTHON SOURCE LINES 65-67
.. code-block:: default
more_smooth_anat_img.to_filename('more_smooth_anat_img.nii.gz')
.. GENERATED FROM PYTHON SOURCE LINES 68-70
Finally, calling plotting.show() is necessary to display the figure
when running as a script outside IPython
.. GENERATED FROM PYTHON SOURCE LINES 70-72
.. code-block:: default
plotting.show()
.. GENERATED FROM PYTHON SOURCE LINES 73-80
|
______
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 3.582 seconds)
**Estimated memory usage:** 170 MB
.. _sphx_glr_download_auto_examples_plot_nilearn_101.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/plot_nilearn_101.ipynb
:alt: Launch binder
: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>`_