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

3D and 4D niimgs: handling and visualizing

3D and 4D niimgs: handling and visualizing

Basic nilearn example: manipulating and looking at data

Basic nilearn example: manipulating and looking at data

Working with Surface images

Working with Surface images

Intro to GLM Analysis: a single-run, single-subject fMRI dataset

Intro to GLM Analysis: a single-run, single-subject fMRI dataset

Glass brain plotting in nilearn

Glass brain plotting in nilearn

Visualizing Megatrawls Network Matrices from Human Connectome Project

Visualizing Megatrawls Network Matrices from Human Connectome Project

Visualizing 4D probabilistic atlas maps

Visualizing 4D probabilistic atlas maps

Basic Atlas plotting

Basic Atlas plotting

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Controlling the contrast of the background when plotting

Controlling the contrast of the background when plotting

Visualizing multiscale functional brain parcellations

Visualizing multiscale functional brain parcellations

Matplotlib colormaps in Nilearn

Matplotlib colormaps in Nilearn

NeuroImaging volumes visualization

NeuroImaging volumes visualization

Plot Haxby masks

Plot Haxby masks

Plotting tools in nilearn

Plotting tools in nilearn

Loading and plotting of a cortical surface atlas

Loading and plotting of a cortical surface atlas

More plotting tools from nilearn

More plotting tools from nilearn

Glass brain plotting in nilearn (all options)

Glass brain plotting in nilearn (all options)

Seed-based connectivity on the surface

Seed-based connectivity on the surface

Making a surface plot of a 3D statistical map

Making a surface plot of a 3D statistical map

Show stimuli of Haxby et al. dataset

Show stimuli of Haxby et al. dataset

Voxel-Based Morphometry on Oasis dataset with Space-Net prior

Voxel-Based Morphometry on Oasis dataset with Space-Net prior

Decoding with FREM: face vs house vs chair object recognition

Decoding with FREM: face vs house vs chair object recognition

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

The haxby dataset: different multi-class strategies

The haxby dataset: different multi-class strategies

Cortical surface-based searchlight decoding

Cortical surface-based searchlight decoding

Searchlight analysis of face vs house recognition

Searchlight analysis of face vs house recognition

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

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

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome

Generate an events.tsv file for the NeuroSpin localizer task

Generate an events.tsv file for the NeuroSpin localizer task

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

Predicted time series and residuals

Predicted time series and residuals

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

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

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

Voxel-Based Morphometry on OASIS dataset

Voxel-Based Morphometry on OASIS dataset

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

Negating an image with math_img

Negating an image with math_img

Comparing the means of 2 images

Comparing the means of 2 images

Smoothing an image

Smoothing an image

Breaking an atlas of labels in separated regions

Breaking an atlas of labels in separated regions

Regions Extraction of Default Mode Networks using Smith Atlas

Regions Extraction of Default Mode Networks using Smith Atlas

Resample an image to a template

Resample an image to a template

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

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

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

NeuroVault meta-analysis of stop-go paradigm studies

NeuroVault meta-analysis of stop-go paradigm studies

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

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding