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.view_img#

nilearn.plotting.view_img(stat_map_img, bg_img='MNI152', cut_coords=None, colorbar=True, title=None, threshold=1e-06, annotate=True, draw_cross=True, black_bg='auto', cmap=<matplotlib.colors.LinearSegmentedColormap object>, symmetric_cmap=True, dim='auto', vmax=None, vmin=None, resampling_interpolation='continuous', opacity=1, **kwargs)[source]#

Interactive html viewer of a statistical map, with optional background.

Parameters

stat_map_imgNiimg-like object

See http://nilearn.github.io/manipulating_images/input_output.html The statistical map image. Can be either a 3D volume or a 4D volume with exactly one time point.

bg_imgNiimg-like object, optional

See input_output. The background image to plot on top of. If nothing is specified, the MNI152 template will be used. To turn off background image, just pass “bg_img=False”. Default=’MNI152’.

cut_coordsNone, or a tuple of floats

The MNI coordinates of the point where the cut is performed as a 3-tuple: (x, y, z). If None is given, the cuts are calculated automatically.

colorbarboolean, optional

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

titlestr, or None, optional

The title displayed on the figure. Default=None.

thresholdstring, number or None, optional

If None is given, the image is not thresholded. If a string of the form “90%” is given, use the 90-th percentile of the absolute value in the image. If a number is given, it is used to threshold the image: values below the threshold (in absolute value) are plotted as transparent. If auto is given, the threshold is determined automatically. Default=1e-6.

annotateboolean, optional

If annotate is True, current cuts are added to the viewer. Default=True.

draw_crossbool, optional

If draw_cross is True, a cross is drawn on the plot to indicate the cut position. Default=True.

black_bgboolean or ‘auto’, optional

If True, the background of the image is set to be black. Otherwise, a white background is used. If set to auto, an educated guess is made to find if the background is white or black. Default=’auto’.

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=`plt.cm.cold_hot`.

symmetric_cmapbool, optional

True: make colormap symmetric (ranging from -vmax to vmax). False: the colormap will go from the minimum of the volume to vmax. Set it to False if you are plotting a positive volume, e.g. an atlas or an anatomical image. Default=True.

dimfloat, or ‘auto’, optional

Dimming factor applied to background image. By default, automatic heuristics are applied based upon the background image intensity. Accepted float values, where a typical span is between -2 and 2 (-2 = increase contrast; 2 = decrease contrast), but larger values can be used for a more pronounced effect. 0 means no dimming. Default=’auto’.

vmaxfloat, or None, optional

max value for mapping colors. If vmax is None and symmetric_cmap is True, vmax is the max absolute value of the volume. If vmax is None and symmetric_cmap is False, vmax is the max value of the volume.

vminfloat, or None, optional

min value for mapping colors. If symmetric_cmap is True, vmin is always equal to -vmax and cannot be chosen. If symmetric_cmap is False, vmin defaults to the min of the image, or 0 when a threshold is used.

resampling_interpolationstr, optional

Interpolation to use when resampling the image to the destination space. Can be:

“continuous”: use 3rd-order spline interpolation

“nearest”: use nearest-neighbor mapping.

Note

“nearest” is faster but can be noisier in some cases.

Default=’continuous’.

opacityfloat in [0,1], optional

The level of opacity of the overlay (0: transparent, 1: opaque). Default=1.

Returns

html_viewthe html viewer object.: It can be saved as an html page html_view.save_as_html(‘test.html’), or opened in a browser html_view.open_in_browser(). If the output is not requested and the current environment is a Jupyter notebook, the viewer will be inserted in the notebook.

Examples using `nilearn.plotting.view_img`#

A introduction tutorial to fMRI decoding

Plotting tools in nilearn

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

nilearn.plotting.view_img#

Examples using nilearn.plotting.view_img#

Examples using `nilearn.plotting.view_img`#