Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.plotting.displays.ZSlicer

class nilearn.plotting.displays.ZSlicer(cut_coords, axes=None, black_bg=False, brain_color=(0.5, 0.5, 0.5), **kwargs)

The ZSlicer class enables axial visualization with plotting functions of Nilearn like nilearn.plotting.plot_img.

This visualization mode can be activated by setting display_mode='z':

from nilearn.datasets import load_mni152_template
from nilearn.plotting import plot_img

img = load_mni152_template()
# display is an instance of the ZSlicer class
display = plot_img(img, display_mode="z")

Attributes:

cut_coords1D ndarray

The cut coordinates.

axesdict of CutAxes

The axes used for plotting.

frame_axesAxes

The axes framing the whole set of views.

See also

nilearn.plotting.displays.XSlicer

Sagittal view

nilearn.plotting.displays.YSlicer

Coronal view

__init__(cut_coords, axes=None, black_bg=False, brain_color=(0.5, 0.5, 0.5), **kwargs)

add_contours(img, threshold=1e-06, filled=False, **kwargs)

Contour a 3D map in all the views.

Parameters:

imgNiimg-like object

See Input and output: neuroimaging data representation. Provides image to plot.

thresholdint or float or None, default=1e-6

Threshold to apply:

• If None is given, the maps are not thresholded.

• If a number is given, it is used to threshold the maps, values below the threshold (in absolute value) are plotted as transparent.

filledbool, default=False

If filled=True, contours are displayed with color fillings.

kwargsdict

Extra keyword arguments are passed to function contour, or function contourf. Useful, arguments are typical “levels”, which is a list of values to use for plotting a contour or contour fillings (if filled=True), and “colors”, which is one color or a list of colors for these contours.

Notes

If colors are not specified, default coloring choices (from matplotlib) for contours and contour_fillings can be different.

add_edges(img, color='r')

Plot the edges of a 3D map in all the views.

Parameters:

imgNiimg-like object

See Input and output: neuroimaging data representation. The 3D map to be plotted. If it is a masked array, only the non-masked part will be plotted.

colormatplotlib color: str or (r, g, b) value, default=’r’

The color used to display the edge map.

add_markers(marker_coords, marker_color='r', marker_size=30, **kwargs)

Add markers to the plot.

Parameters:

marker_coordsndarray of shape (n_markers, 3)

Coordinates of the markers to plot. For each slice, only markers that are 2 millimeters away from the slice are plotted.

marker_colorpyplot compatible color or list of shape (n_markers,), default=’r’

List of colors for each marker that can be string or matplotlib colors.

marker_sizefloat or list of float of shape (n_markers,), default=30

Size in pixel for each marker.

add_overlay(img, threshold=1e-06, colorbar=False, cbar_tick_format='%.2g', cbar_vmin=None, cbar_vmax=None, **kwargs)

Plot a 3D map in all the views.

Parameters:

imgNiimg-like object

See Input and output: neuroimaging data representation. If it is a masked array, only the non-masked part will be plotted.

thresholdint or float or None, default=1e-6

Threshold to apply:

• If None is given, the maps are not thresholded.

• If a number is given, it is used to threshold the maps: values below the threshold (in absolute value) are plotted as transparent.

cbar_tick_format: str, default=”%.2g” (scientific notation)

Controls how to format the tick labels of the colorbar. Ex: use “%i” to display as integers.

colorbarbool, default=False

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

kwargsdict

Extra keyword arguments are passed to function imshow.

cbar_vminfloat, optional

Minimal value for the colorbar. If None, the minimal value is computed based on the data.

cbar_vmaxfloat, optional

Maximal value for the colorbar. If None, the maximal value is computed based on the data.

annotate(left_right=True, positions=True, scalebar=False, size=12, scale_size=5.0, scale_units='cm', scale_loc=4, decimals=0, **kwargs)

Add annotations to the plot.

Parameters:

left_rightbool, default=True

If True, annotations indicating which side is left and which side is right are drawn.

positionsbool, default=True

If True, annotations indicating the positions of the cuts are drawn.

scalebarbool, default=False

If True, cuts are annotated with a reference scale bar. For finer control of the scale bar, please check out the draw_scale_bar method on the axes in “axes” attribute of this object.

sizeint, default=12

The size of the text used.

scale_sizeint or float, default=5.0

The length of the scalebar, in units of scale_units.

scale_units{‘cm’, ‘mm’}, default=’cm’

The units for the scalebar.

scale_locint, default=4

The positioning for the scalebar. Valid location codes are:

• 1: “upper right”

• 2: “upper left”

• 3: “lower left”

• 4: “lower right”

• 5: “right”

• 6: “center left”

• 7: “center right”

• 8: “lower center”

• 9: “upper center”

• 10: “center”

decimalsint, default=0

Number of decimal places on slice position annotation. If zero, the slice position is integer without decimal point.

kwargsdict

Extra keyword arguments are passed to matplotlib’s text function.

property black_bg

Return black background.

property brain_color

Return brain color.

close()

Close the figure.

This is necessary to avoid leaking memory.

draw_cross(cut_coords=None, **kwargs)

Draw a crossbar on the plot to show where the cut is performed.

Parameters:

cut_coords3-tuple of float, optional

The position of the cross to draw. If None is passed, the OrthoSlicer’s cut coordinates are used.

kwargsdict

Extra keyword arguments are passed to function matplotlib.pyplot.axhline.

classmethod find_cut_coords(img=None, threshold=None, cut_coords=None)

Instantiate the slicer and find cut coordinates.

Parameters:

img3D Nifti1Image

The brain map.

thresholdfloat, optional

The lower threshold to the positive activation. If None, the activation threshold is computed using the 80% percentile of the absolute value of the map.

cut_coordslist of float, optional

xyz world coordinates of cuts.

Returns:

cut_coordslist of float

xyz world coordinates of cuts.

classmethod init_with_figure(img, threshold=None, cut_coords=None, figure=None, axes=None, black_bg=False, leave_space=False, colorbar=False, brain_color=(0.5, 0.5, 0.5), **kwargs)

Initialize the slicer with an image.

Parameters:

imgNiimg-like object

See Input and output: neuroimaging data representation.

cut_coords3 tuple of int

The cut position, in world space.

axesmatplotlib.axes.Axes, optional

The axes that will be subdivided in 3.

black_bgbool, default=False

If True, the background of the figure will be put to black. If you wish to save figures with a black background, you will need to pass facecolor='k', edgecolor='k' to matplotlib.pyplot.savefig.

brain_colortuple, default=(0.5, 0.5, 0.5)

The brain color to use as the background color (e.g., for transparent colorbars).

savefig(filename, dpi=None)

Save the figure to a file.

Parameters:

filenamestr

The file name to save to. Its extension determines the file type, typically ‘.png’, ‘.svg’ or ‘.pdf’.

dpiNone or scalar, default=None

The resolution in dots per inch.

title(text, x=0.01, y=0.99, size=15, color=None, bgcolor=None, alpha=1, **kwargs)

Write a title to the view.

Parameters:

textstr

The text of the title.

xfloat, default=0.01

The horizontal position of the title on the frame in fraction of the frame width.

yfloat, default=0.99

The vertical position of the title on the frame in fraction of the frame height.

sizeint, default=15

The size of the title text.

colormatplotlib color specifier, optional

The color of the font of the title.

bgcolormatplotlib color specifier, optional

The color of the background of the title.

alphafloat, default=1

The alpha value for the background.

kwargs

Extra keyword arguments are passed to matplotlib’s text function.

Examples using nilearn.plotting.displays.ZSlicer

Plot Haxby masks

Plot Haxby masks

More plotting tools from nilearn

More plotting tools from nilearn

Voxel-Based Morphometry on Oasis dataset

Voxel-Based Morphometry on Oasis dataset

Encoding models for visual stimuli from Miyawaki et al. 2008

Encoding models for visual stimuli from Miyawaki et al. 2008

Predicted time series and residuals

Predicted time series and residuals

Voxel-Based Morphometry on OASIS dataset

Voxel-Based Morphometry on OASIS dataset

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of face vs house recognition

Massively univariate analysis of face vs house recognition