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.plot_design_matrix#

nilearn.plotting.plot_design_matrix(design_matrix, rescale=True, ax=None, output_file=None)[source]#

Plot a design matrix provided as a pandas.DataFrame.

Parameters:
design matrixpandas.DataFrame

Describes a design matrix.

rescalebool, optional

Rescale columns magnitude for visualization or not. Default=True.

axmatplotlib.axes.Axes, optional

Handle to axes onto which we will draw the design matrix.

output_filestr, or None, optional

The name of an image file to export the plot to. Valid extensions are .png, .pdf, .svg. If output_file is not None, the plot is saved to a file, and the display is closed.

Returns:
axmatplotlib.axes.Axes

The axes used for plotting.

Examples using nilearn.plotting.plot_design_matrix#

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

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

Examples of design matrices

Examples of design matrices

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Understanding parameters of the first-level model

Understanding parameters of the first-level model

Example of second level design matrix

Example of second level design matrix

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

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

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