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_contrast_matrix#

nilearn.plotting.plot_contrast_matrix(contrast_def, design_matrix, colorbar=False, ax=None, output_file=None)[source]#

Create plot for contrast definition.

Parameters:
contrast_defstr or numpy.ndarray of shape[1] <= n_col where n_col is the number of columns of the design matrix.

The string can be a formula compatible with pandas.DataFrame.eval. Basically one can use the name of the conditions as they appear in the design matrix of the fitted model combined with operators +- and combined with numbers with operators +-*/.

design_matrixpandas.DataFrame

Design matrix to use.

colorbarbool, optional

If True, display a colorbar on the right of the plots. Default=False.

axmatplotlib.axes.Axes, optional

Axis on which to plot the figure. If None, a new figure will be created.

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

Figure object.

Examples using nilearn.plotting.plot_contrast_matrix#

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

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

First level analysis of a complete BIDS dataset from openneuro

First level analysis of a complete BIDS dataset from openneuro

Understanding parameters of the first-level model

Understanding parameters of the first-level model