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

An introduction tutorial to fMRI decoding

An introduction tutorial to fMRI decoding

Basic nilearn example: manipulating and looking at data

Basic nilearn example: manipulating and looking at data

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

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

Basic Atlas plotting

Basic Atlas plotting

Colormaps in Nilearn

Colormaps in Nilearn

Controlling the contrast of the background when plotting

Controlling the contrast of the background when plotting

Glass brain plotting in nilearn

Glass brain plotting in nilearn

Glass brain plotting in nilearn (all options)

Glass brain plotting in nilearn (all options)

Loading and plotting of a cortical surface atlas

Loading and plotting of a cortical surface atlas

Making a surface plot of a 3D statistical map

Making a surface plot of a 3D statistical map

More plotting tools from nilearn

More plotting tools from nilearn

NeuroImaging volumes visualization

NeuroImaging volumes visualization

Plot Haxby masks

Plot Haxby masks

Plotting images with transparent thresholding

Plotting images with transparent thresholding

Plotting tools in nilearn

Plotting tools in nilearn

Seed-based connectivity on the surface

Seed-based connectivity on the surface

Using brain templates from TemplateFlow

Using brain templates from TemplateFlow

Visualizing 4D probabilistic atlas maps

Visualizing 4D probabilistic atlas maps

Visualizing Megatrawls Network Matrices from Human Connectome Project

Visualizing Megatrawls Network Matrices from Human Connectome Project

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

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

Visualizing global patterns with a carpet plot

Visualizing global patterns with a carpet plot

Visualizing multiscale functional brain parcellations

Visualizing multiscale functional brain parcellations

Cortical surface-based searchlight decoding

Cortical surface-based searchlight decoding

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

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

Decoding with FREM: face vs house vs chair object recognition

Decoding with FREM: face vs house vs chair object recognition

Different classifiers in decoding the Haxby dataset

Different classifiers in decoding the Haxby dataset

Encoding models for visual stimuli from Miyawaki et al. 2008

Encoding models for visual stimuli from Miyawaki et al. 2008

Example of pattern recognition on simulated data

Example of pattern recognition on simulated data

FREM on Jimura et al “mixed gambles” dataset

FREM on Jimura et al "mixed gambles" dataset

ROI-based decoding analysis in the Haxby dataset

ROI-based decoding analysis in the Haxby dataset

Reconstruction of visual stimuli from Miyawaki et al. 2008

Reconstruction of visual stimuli from Miyawaki et al. 2008

Searchlight analysis of face vs house recognition

Searchlight analysis of face vs house recognition

Show stimuli of Haxby et al. dataset

Show stimuli of Haxby et al. dataset

The Haxby dataset: different multi-class strategies

The Haxby dataset: different multi-class strategies

Understanding Decoder

Understanding Decoder

Voxel-Based Morphometry on Oasis dataset

Voxel-Based Morphometry on Oasis dataset

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

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

Classification of age groups using functional connectivity

Classification of age groups using functional connectivity

Clustering methods to learn a brain parcellation from fMRI

Clustering methods to learn a brain parcellation from fMRI

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Computing a connectome with sparse inverse covariance

Computing a connectome with sparse inverse covariance

Connectivity structure estimation on simulated data

Connectivity structure estimation on simulated data

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

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

Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome

Extracting signals from a brain parcellation

Extracting signals from a brain parcellation

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions

Group Sparse inverse covariance for multi-subject connectome

Group Sparse inverse covariance for multi-subject connectome

Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes

Example of surface-based first-level analysis

Example of surface-based first-level analysis

First level analysis of a complete BIDS dataset from openneuro

First level analysis of a complete BIDS dataset from openneuro

Generate an events.tsv file for the NeuroSpin localizer task

Generate an events.tsv file for the NeuroSpin localizer task

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

Single-subject data (two runs) in native space

Single-subject data (two runs) in native space

Example of generic design in second-level models

Example of generic design in second-level models

Second-level fMRI model: one sample test

Second-level fMRI model: one sample test

Second-level fMRI model: true positive proportion in clusters

Second-level fMRI model: true positive proportion in clusters

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

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

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

Breaking an atlas of labels in separated regions

Breaking an atlas of labels in separated regions

Comparing the means of 2 images

Comparing the means of 2 images

Computing a Region of Interest (ROI) mask manually

Computing a Region of Interest (ROI) mask manually

Extracting signals from brain regions using the NiftiLabelsMasker

Extracting signals from brain regions using the NiftiLabelsMasker

Image thresholding

Image thresholding

Negating an image with math_img

Negating an image with math_img

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

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

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

Smoothing an image

Smoothing an image

Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation

Visualization of affine resamplings

Visualization of affine resamplings

A short demo of the surface images & maskers

A short demo of the surface images & maskers

Advanced decoding using scikit-learn

Advanced decoding using scikit-learn

BIDS dataset first- and second-level analysis

BIDS dataset first- and second-level analysis

Beta-Series Modeling for Task-Based Functional Connectivity

Beta-Series Modeling for Task-Based Functional Connectivity

Independent Component Analysis (ICA) of NeuroVault maps

Independent Component Analysis (ICA) of NeuroVault maps

Independent Component Analysis (ICA) of fMRI timeseries

Independent Component Analysis (ICA) of fMRI timeseries

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a motor task from the Localizer dataset

Massively univariate analysis of a motor task from the Localizer dataset

Massively univariate analysis of a visual task from the Haxby dataset

Massively univariate analysis of a visual task from the Haxby dataset

NeuroVault meta-analysis of stop-go paradigm studies

NeuroVault meta-analysis of stop-go paradigm studies

SearchLight with a custom correlation estimator

SearchLight with a custom correlation estimator