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_carpet(img, mask_img=None, mask_labels=None, detrend=True, output_file=None, figure=None, axes=None, vmin=None, vmax=None, title=None, cmap=<matplotlib.colors.LinearSegmentedColormap object>)¶
Plot an image representation of voxel intensities across time.
This figure is also known as a “grayplot” or “Power plot”.
- imgNiimg-like object
See input-output. 4D image.
- mask_imgNiimg-like object or None, optional
Limit plotted voxels to those inside the provided mask (default is None). If a 3D atlas is provided, voxels will be grouped by atlas value and a colorbar will be added to the left side of the figure with atlas labels. If not specified, a new mask will be derived from data. See http://nilearn.github.io/manipulating_images/input_output.html.
mask_imgcorresponds to an atlas, then this dictionary maps values from the
mask_imgto labels. Dictionary keys are labels and values are values within the atlas.
Detrend and z-score the data prior to plotting. Default=True.
str, or None, optional
The name of an image file to export the plot to. Valid extensions are .png, .pdf, .svg. If
output_fileis not None, the plot is saved to a file, and the display is closed.
matplotlib.figure.Figure, or None, optional
Matplotlib figure used or its number. If
Noneis given, a new figure is created.
matplotlib.axes.Axes, or 4 tupleof
float: (xmin, ymin, width, height), optional
The axes, or the coordinates, in matplotlib figure space, of the axes used to display the plot. If
None, the complete figure is used.
Lower bound of the colormap. If
None, the min of the image is used. Passed to
Upper bound of the colormap. If
None, the max of the image is used. Passed to
str, or None, optional
The title displayed on the figure. Default=None.
The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object.
This argumeent is used only if an atlas is used.
Figure object with carpet plot.
This figure was originally developed in 1.
In cases of long acquisitions (>800 volumes), the data will be downsampled to have fewer than 800 volumes before being plotted.
Jonathan D. Power. A simple but useful way to assess fmri scan qualities. NeuroImage, 154:150–158, 2017. Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies. URL: https://www.sciencedirect.com/science/article/pii/S1053811916303871, doi:https://doi.org/10.1016/j.neuroimage.2016.08.009.