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.
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¶
Basic nilearn example: manipulating and looking at data
Intro to GLM Analysis: a single-run, single-subject fMRI dataset
Controlling the contrast of the background when plotting
Visualizing Megatrawls Network Matrices from Human Connectome Project
Visualizing multiscale functional brain parcellations
Decoding with fast ensembling of regularized models
Regression with fast ensembling of regularized models
Classification of age groups using functional connectivity
Clustering methods to learn a brain parcellation from fMRI
Comparing connectomes on different reference atlases
Computing a connectome with sparse inverse covariance
Connectivity structure estimation on simulated data
Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
Extracting signals of a probabilistic atlas of functional regions
Group Sparse inverse covariance for multi-subject connectome
Regions extraction using dictionary learning and functional connectomes
First level analysis of a complete BIDS dataset from openneuro
Second-level fMRI model: true positive proportion in clusters
Second-level fMRI model: two-sample test, unpaired and paired
Computing a Region of Interest (ROI) mask manually
Extracting signals from brain regions using the NiftiLabelsMasker
Beta-Series Modeling for Task-Based Functional Connectivity
Independent Component Analysis (ICA) of NeuroVault maps
Independent Component Analysis (ICA) of fMRI timeseries
Massively univariate analysis of a calculation task
NeuroVault meta-analysis of stop-go paradigm studies
