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_correlation

nilearn.plotting.plot_design_matrix_correlation(design_matrix, tri='full', cmap='RdBu_r', colorbar=True, output_file=None, **kwargs)

Compute and plot the correlation between regressor of a design matrix.

The drift and constant regressors are omitted from the plot.

Added in Nilearn 0.11.0.

Parameters:

design_matrixpandas.DataFrame, pandas.DataFrame pathlib.Path

Design matrix whose correlation matrix you want to plot.

tri{“full”, “diag”}, default=”full”

Which triangular part of the matrix to plot:

• "diag": Plot the lower part with the diagonal

• "full": Plot the full matrix

cmapmatplotlib.colors.Colormap, or str, optional

The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object. default=”RdBu_r”

This must be a diverging colormap as the correlation matrix will be centered on 0. The allowed colormaps are:

• "bwr"

• "RdBu_r"

• "seismic_r"

colorbarbool, optional

If True, display a colorbar next to the plots.

output_filestr or pathlib.Path or None, default=None

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.

kwargsextra keyword arguments, optional

Extra keyword arguments are sent to nilearn.plotting.plot_matrix

Returns:

displaymatplotlib.axes.Axes

Axes image.

Examples

>>> import numpy as np
>>> from pandas import DataFrame
>>> from nilearn.glm.first_level import make_first_level_design_matrix
>>> from nilearn.plotting import plot_design_matrix_correlation
>>> from nilearn.plotting.image.img_plotting import show
>>>
>>> #creating a design matrix
>>>
>>> frame_times = np.arange(25)
>>> onsets = np.arange(9)
>>> duration = np.linspace(1, 9, 9)
>>> trial_type = ["ET_0", "ET_0", "ET_0",
...               "ET_1", "ET_1", "ET_1",
...               "ET_2", "ET_2", "ET_2"]
>>> events = DataFrame({"trial_type": trial_type,
...                     "onset": onsets,
...                     "duration": duration})
>>>
>>> design_matrix = make_first_level_design_matrix(frame_times, events)
>>>
>>> ax = plot_design_matrix_correlation(design_matrix)
>>> show()

Examples using nilearn.plotting.plot_design_matrix_correlation

Examples of design matrices

Examples of design matrices