.. 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_prob_atlas.py:
Visualizing 4D probabilistic atlas maps
=======================================
This example shows how to visualize probabilistic atlases made of 4D images.
There are 3 different display types:
1. "contours", which means maps or ROIs are shown as contours delineated by colored lines.
2. "filled_contours", maps are shown as contours same as above but with fillings inside the contours.
3. "continuous", maps are shown as just color overlays.
A colorbar can optionally be added.
The :func:`nilearn.plotting.plot_prob_atlas` function displays each map
with each different color which are picked randomly from the colormap
which is already defined.
See :ref:`plotting` for more information to know how to tune the parameters.
.. rst-class:: sphx-glr-horizontal
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_001.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_002.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_003.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_004.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_005.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_006.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_007.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_008.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_009.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_010.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_011.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
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.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_012.png
:alt: plot prob atlas
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/usr/lib/python3/dist-packages/numpy/lib/npyio.py:2358: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
output = genfromtxt(fname, **kwargs)
Dataset created in /home/varoquau/nilearn_data/allen_rsn_2011
Downloading data from https://osf.io/hrcku/download ...
...done. (3 seconds, 0 min)
Extracting data from /home/varoquau/nilearn_data/allen_rsn_2011/5f8f4a26df3a57a30ddc4285d4d5c6c2/download..... done.
Dataset created in /home/varoquau/nilearn_data/pauli_2017
Downloading data from https://osf.io/w8zq2/download ...
...done. (3 seconds, 0 min)
Downloading data from https://osf.io/6qrcb/download ...
...done. (3 seconds, 0 min)
/home/varoquau/dev/nilearn/nilearn/plotting/displays.py:97: UserWarning: No contour levels were found within the data range.
im = getattr(ax, type)(data_2d.copy(),
/home/varoquau/dev/nilearn/nilearn/plotting/displays.py:97: UserWarning: No contour levels were found within the data range.
im = getattr(ax, type)(data_2d.copy(),
/usr/lib/python3/dist-packages/numpy/ma/core.py:2785: UserWarning: Warning: converting a masked element to nan.
_data = np.array(data, dtype=dtype, copy=copy,
/home/varoquau/dev/nilearn/nilearn/plotting/displays.py:97: UserWarning: linewidths is ignored by contourf
im = getattr(ax, type)(data_2d.copy(),
ready
|
.. code-block:: default
# Load 4D probabilistic atlases
from nilearn import datasets
# Harvard Oxford Atlasf
harvard_oxford = datasets.fetch_atlas_harvard_oxford('cort-prob-2mm')
harvard_oxford_sub = datasets.fetch_atlas_harvard_oxford('sub-prob-2mm')
# Multi Subject Dictionary Learning Atlas
msdl = datasets.fetch_atlas_msdl()
# Smith ICA Atlas and Brain Maps 2009
smith = datasets.fetch_atlas_smith_2009()
# ICBM tissue probability
icbm = datasets.fetch_icbm152_2009()
# Allen RSN networks
allen = datasets.fetch_atlas_allen_2011()
# Pauli subcortical atlas
subcortex = datasets.fetch_atlas_pauli_2017()
# Visualization
from nilearn import plotting
atlas_types = {'Harvard_Oxford': harvard_oxford.maps,
'Harvard_Oxford sub': harvard_oxford_sub.maps,
'MSDL': msdl.maps, 'Smith 2009 10 RSNs': smith.rsn10,
'Smith2009 20 RSNs': smith.rsn20,
'Smith2009 70 RSNs': smith.rsn70,
'Smith2009 20 Brainmap': smith.bm20,
'Smith2009 70 Brainmap': smith.bm70,
'ICBM tissues': (icbm['wm'], icbm['gm'], icbm['csf']),
'Allen2011': allen.rsn28,
'Pauli2017 Subcortical Atlas': subcortex.maps,
}
for name, atlas in sorted(atlas_types.items()):
plotting.plot_prob_atlas(atlas, title=name)
# An optional colorbar can be set
plotting.plot_prob_atlas(smith.bm10, title='Smith2009 10 Brainmap (with'
' colorbar)',
colorbar=True)
print('ready')
plotting.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 1 minutes 59.129 seconds)
.. _sphx_glr_download_auto_examples_01_plotting_plot_prob_atlas.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_prob_atlas.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_prob_atlas.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_prob_atlas.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_