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()

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

Basic nilearn example: manipulating and looking at data

Basic nilearn example: manipulating and looking at data

ROI-based decoding analysis in Haxby et al. dataset

ROI-based decoding analysis in Haxby et al. dataset

Setting a parameter by cross-validation

Setting a parameter by cross-validation

Voxel-Based Morphometry on Oasis dataset

Voxel-Based Morphometry on Oasis dataset

Different classifiers in decoding the Haxby dataset

Different classifiers in decoding the Haxby dataset

Example of pattern recognition on simulated data

Example of pattern recognition on simulated data

Reconstruction of visual stimuli from Miyawaki et al. 2008

Reconstruction of visual stimuli from Miyawaki et al. 2008

Computing a connectome with sparse inverse covariance

Computing a connectome with sparse inverse covariance

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions

Connectivity structure estimation on simulated data

Connectivity structure estimation on simulated data

Group Sparse inverse covariance for multi-subject connectome

Group Sparse inverse covariance for multi-subject connectome

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Classification of age groups using functional connectivity

Classification of age groups using functional connectivity

Extracting signals from a brain parcellation

Extracting signals from a brain parcellation

Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome

Single-subject data (two runs) in native space

Single-subject data (two runs) in native space

Example of surface-based first-level analysis

Example of surface-based first-level analysis

Simple example of two-runs fMRI model fitting

Simple example of two-runs fMRI model fitting

Second-level fMRI model: true positive proportion in clusters

Second-level fMRI model: true positive proportion in clusters

Statistical testing of a second-level analysis

Statistical testing of a second-level analysis

Voxel-Based Morphometry on OASIS dataset

Voxel-Based Morphometry on OASIS dataset

Second-level fMRI model: two-sample test, unpaired and paired

Second-level fMRI model: two-sample test, unpaired and paired

Second-level fMRI model: one sample test

Second-level fMRI model: one sample test

Example of generic design in second-level models

Example of generic design in second-level models

Resample an image to a template

Resample an image to a template

Regions Extraction of Default Mode Networks using Smith Atlas

Regions Extraction of Default Mode Networks using Smith Atlas

Simple example of NiftiMasker use

Simple example of NiftiMasker use

Region Extraction using a t-statistical map (3D)

Region Extraction using a t-statistical map (3D)

Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation

Visualization of affine resamplings

Visualization of affine resamplings

Computing a Region of Interest (ROI) mask manually

Computing a Region of Interest (ROI) mask manually

Multivariate decompositions: Independent component analysis of fMRI

Multivariate decompositions: Independent component analysis of fMRI

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a calculation task from the Localizer dataset

BIDS dataset first and second level analysis

BIDS dataset first and second level analysis

NeuroVault meta-analysis of stop-go paradigm studies

NeuroVault meta-analysis of stop-go paradigm studies

Surface-based dataset first and second level analysis of a dataset

Surface-based dataset first and second level analysis of a dataset

NeuroVault cross-study ICA maps

NeuroVault cross-study ICA maps

Massively univariate analysis of face vs house recognition

Massively univariate analysis of face vs house recognition

A short demo of the surface images & maskers

A short demo of the surface images & maskers