Note
This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
nilearn.plotting.show#
- nilearn.plotting.show()[source]#
Show all the figures generated by nilearn and/or matplotlib.
This function is equivalent to
matplotlib.pyplot.show
, but is skipped on the ‘Agg’ backend where it has no effect other than to emit a warning.
Examples using nilearn.plotting.show
#
![](../../_images/sphx_glr_plot_nilearn_101_thumb.png)
Basic nilearn example: manipulating and looking at data
![](../../_images/sphx_glr_plot_haxby_full_analysis_thumb.png)
ROI-based decoding analysis in Haxby et al. dataset
![](../../_images/sphx_glr_plot_haxby_different_estimators_thumb.png)
Different classifiers in decoding the Haxby dataset
![](../../_images/sphx_glr_plot_miyawaki_reconstruction_thumb.png)
Reconstruction of visual stimuli from Miyawaki et al. 2008
![](../../_images/sphx_glr_plot_inverse_covariance_connectome_thumb.png)
Computing a connectome with sparse inverse covariance
![](../../_images/sphx_glr_plot_probabilistic_atlas_extraction_thumb.png)
Extracting signals of a probabilistic atlas of functional regions
![](../../_images/sphx_glr_plot_simulated_connectome_thumb.png)
Connectivity structure estimation on simulated data
![](../../_images/sphx_glr_plot_multi_subject_connectome_thumb.png)
Group Sparse inverse covariance for multi-subject connectome
![](../../_images/sphx_glr_plot_compare_decomposition_thumb.png)
Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
![](../../_images/sphx_glr_plot_extract_regions_dictlearning_maps_thumb.png)
Regions extraction using dictionary learning and functional connectomes
![](../../_images/sphx_glr_plot_atlas_comparison_thumb.png)
Comparing connectomes on different reference atlases
![](../../_images/sphx_glr_plot_group_level_connectivity_thumb.png)
Classification of age groups using functional connectivity
![](../../_images/sphx_glr_plot_proportion_activated_voxels_thumb.png)
Second-level fMRI model: true positive proportion in clusters
![](../../_images/sphx_glr_plot_second_level_two_sample_test_thumb.png)
Second-level fMRI model: two-sample test, unpaired and paired
![](../../_images/sphx_glr_plot_extract_rois_smith_atlas_thumb.png)
Regions Extraction of Default Mode Networks using Smith Atlas
![](../../_images/sphx_glr_plot_roi_extraction_thumb.png)
Computing a Region of Interest (ROI) mask manually
![](../../_images/sphx_glr_plot_ica_resting_state_thumb.png)
Multivariate decompositions: Independent component analysis of fMRI
![](../../_images/sphx_glr_plot_localizer_simple_analysis_thumb.png)
Massively univariate analysis of a calculation task from the Localizer dataset
![](../../_images/sphx_glr_plot_neurovault_meta_analysis_thumb.png)
NeuroVault meta-analysis of stop-go paradigm studies
![](../../_images/sphx_glr_plot_surface_bids_analysis_thumb.png)
Surface-based dataset first and second level analysis of a dataset
![](../../_images/sphx_glr_plot_haxby_mass_univariate_thumb.png)
Massively univariate analysis of face vs house recognition