.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` 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_01_plotting_plot_demo_plotting.py:
Plotting tools in nilearn
==========================
Nilearn comes with a set of plotting functions for easy visualization of
Nifti-like images such as statistical maps mapped onto anatomical images
or onto glass brain representation, anatomical images, functional/EPI images,
region specific mask images.
See :ref:`plotting` for more details.
Retrieve data from nilearn provided (general-purpose) datasets
---------------------------------------------------------------
.. code-block:: default
from nilearn import datasets
# haxby dataset to have EPI images and masks
haxby_dataset = datasets.fetch_haxby()
# print basic information on the dataset
print('First subject anatomical nifti image (3D) is at: %s' %
haxby_dataset.anat[0])
print('First subject functional nifti image (4D) is at: %s' %
haxby_dataset.func[0]) # 4D data
haxby_anat_filename = haxby_dataset.anat[0]
haxby_mask_filename = haxby_dataset.mask_vt[0]
haxby_func_filename = haxby_dataset.func[0]
# one motor contrast map from NeuroVault
motor_images = datasets.fetch_neurovault_motor_task()
stat_img = motor_images.images[0]
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
First subject anatomical nifti image (3D) is at: /home/varoquau/nilearn_data/haxby2001/subj2/anat.nii.gz
First subject functional nifti image (4D) is at: /home/varoquau/nilearn_data/haxby2001/subj2/bold.nii.gz
Plotting statistical maps with function `plot_stat_map`
--------------------------------------------------------
.. code-block:: default
from nilearn import plotting
# Visualizing t-map image on EPI template with manual
# positioning of coordinates using cut_coords given as a list
plotting.plot_stat_map(stat_img,
threshold=3, title="plot_stat_map",
cut_coords=[36, -27, 66])
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_demo_plotting_001.png
:alt: plot demo plotting
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Making interactive visualizations with function `view_img`
----------------------------------------------------------
An alternative to :func:`nilearn.plotting.plot_stat_map` is to use
:func:`nilearn.plotting.view_img` that gives more interactive
visualizations in a web browser. See :ref:`interactive-stat-map-plotting`
for more details.
.. code-block:: default
view = plotting.view_img(stat_img, threshold=3)
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view
.. only:: builder_html
.. raw:: html
.. code-block:: default
# uncomment this to open the plot in a web browser:
# view.open_in_browser()
Plotting statistical maps in a glass brain with function `plot_glass_brain`
---------------------------------------------------------------------------
Now, the t-map image is mapped on glass brain representation where glass
brain is always a fixed background template
.. code-block:: default
plotting.plot_glass_brain(stat_img, title='plot_glass_brain',
threshold=3)
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_demo_plotting_002.png
:alt: plot demo plotting
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Plotting anatomical images with function `plot_anat`
-----------------------------------------------------
Visualizing anatomical image of haxby dataset
.. code-block:: default
plotting.plot_anat(haxby_anat_filename, title="plot_anat")
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_demo_plotting_003.png
:alt: plot demo plotting
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Plotting ROIs (here the mask) with function `plot_roi`
-------------------------------------------------------
Visualizing ventral temporal region image from haxby dataset overlayed on
subject specific anatomical image with coordinates positioned automatically on
region of interest (roi)
.. code-block:: default
plotting.plot_roi(haxby_mask_filename, bg_img=haxby_anat_filename,
title="plot_roi")
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_demo_plotting_004.png
:alt: plot demo plotting
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Plotting EPI image with function `plot_epi`
---------------------------------------------
.. code-block:: default
# Import image processing tool
from nilearn import image
# Compute the voxel_wise mean of functional images across time.
# Basically reducing the functional image from 4D to 3D
mean_haxby_img = image.mean_img(haxby_func_filename)
# Visualizing mean image (3D)
plotting.plot_epi(mean_haxby_img, title="plot_epi")
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_demo_plotting_005.png
:alt: plot demo plotting
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
A call to plotting.show is needed to display the plots when running
in script mode (ie outside IPython)
.. code-block:: default
plotting.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 10.025 seconds)
.. _sphx_glr_download_auto_examples_01_plotting_plot_demo_plotting.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/01_plotting/plot_demo_plotting.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_demo_plotting.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_demo_plotting.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_