class visbrain.objects.ImageObj(name, data=None, xaxis=None, yaxis=None, cmap='viridis', clim=None, vmin=None, under='gray', vmax=None, over='red', interpolation='nearest', max_pts=-1, parent=None, transform=None, verbose=None, **kw)[source][source]

Create a single image object.

data : array_like

Array of data. If data.ndim in [1, 2] the color is inferred from the data. Otherwise, if data.ndim is 3, data is interpreted as color if the last dimension is either 3 (RGB) or 4 (RGBA).

xaxis : array_like | None

Vector to use for the x-axis (number of columns in the image). If None, xaxis is inferred from the second dimension of data.

yaxis : array_like | None

Vector to use for the y-axis (number of rows in the image). If None, yaxis is inferred from the first dimension of data.

clim : tuple | None

Colorbar limits. If None, clim=(data.min(), data.max())

cmap : string | None

Colormap name.

vmin : float | None

Minimum threshold of the colorbar.

under : string/tuple/array_like | None

Color for values under vmin.

vmax : float | None

Maximum threshold of the colorbar.

under : string/tuple/array_like | None

Color for values over vmax.

interpolation : string | ‘nearest’

Interpolation method for the image. See vispy.scene.visuals.Image for availables interpolation methods.

max_pts : int | -1

Maximum number of points of the image along the x or y axis. This parameter is essentially used to solve OpenGL issues with very large images.

transform : VisPy.visuals.transforms | None

VisPy transformation to set to the parent node.

parent : VisPy.parent | None

Markers object parent.

verbose : string

Verbosity level.

kw : dict | {}

Optional arguments are used to control the colorbar (See ColorbarObj).


List of supported shortcuts :

  • s : save the figure
  • <delete> : reset camera


>>> import numpy as np
>>> from visbrain.objects import ImageObj
>>> n = 100
>>> time = np.r_[np.arange(n - 1), np.arange(n)[::-1]]
>>> time = time.reshape(-1, 1) + time.reshape(1, -1)
>>> im = ImageObj('im', time, cmap='Spectral_r', interpolation='bicubic')
>>> im.preview(axis=True)


__init__(name[, data, xaxis, yaxis, cmap, …]) Init.
describe_tree() Tree description.
preview([bgcolor, axis, xyz, show, obj, size]) Previsualize the result.
screenshot(saveas[, print_size, dpi, unit, …]) Take a screeshot of the scene.
set_data(data[, xaxis, yaxis, clim, cmap, …]) Set data to the image.
set_shortcuts_to_canvas(canvas) Set shortcuts to a VisbrainCanvas.
to_dict() Return a dictionary of all colorbar args.
to_kwargs([addisminmax]) Return a dictionary for input arguments.
update() Fonction to run when an update is needed.
update_from_dict(kwargs) Update attributes from a dictionary.

Get the clim value.


Get the cmap value.


Get the interpolation value.


Get the name value.


Get the over value.


Get the parent value.

preview(bgcolor='black', axis=False, xyz=False, show=True, obj=None, size=(1200, 800), **kwargs)[source]

Previsualize the result.

bgcolor : array_like/string/tuple | ‘black’

Background color for the preview.

axis : bool | False

Add x and y axis with ticks.

xyz : bool | False

Add an (x, y, z) axis to the scene.

obj : VisbrainObj | None

Pass a Visbrain object if you want to use the camera of an other object.

size : tuple | (1200, 800)

Default size of the window.

kwargs : dict | {}

Optional arguments are passed to the VisbrainCanvas class.

screenshot(saveas, print_size=None, dpi=300.0, unit='centimeter', factor=None, region=None, autocrop=False, bgcolor=None, transparent=False, obj=None, line_width=1.0, **kwargs)[source]

Take a screeshot of the scene.

By default, the rendered canvas will have the size of your screen. The screenshot() method provides two ways to increase to exported image resolution :

  • Using print_size, unit and dpi inputs : specify the size of the image at a specific dpi level. For example, you might want to have an (10cm, 15cm) image at 300 dpi.
  • Using the factor input : multiply the default image size by this factor. For example, if you have a (1920, 1080) monitor and if factor is 2, the exported image should have a shape of (3840, 2160) pixels.
saveas : str

The name of the file to be saved. This file must contains a extension like .png, .tiff, .jpg…

print_size : tuple | None

The desired print size. This argument should be used in association with the dpi and unit inputs. print_size describe should be a tuple of two floats describing (width, height) of the exported image for a specific dpi level. The final image might not have the exact desired size but will try instead to find a compromize regarding to the proportion of width/height of the original image.

dpi : float | 300.

Dots per inch for printing the image.

unit : {‘centimeter’, ‘millimeter’, ‘pixel’, ‘inch’}

Unit of the printed size.

factor : float | None

If you don’t want to use the print_size input, factor simply multiply the resolution of your screen.

region : tuple | None

Select a specific region. Must be a tuple of four integers each one describing (x_start, y_start, width, height).

autocrop : bool | False

Automaticaly crop the figure in order to have the smallest space between the brain and the border of the picture.

bgcolor : array_like/string | None

The background color of the image.

transparent : bool | False

Specify if the exported figure have to contains a transparent background.

obj : VisbrainObj | None

Pass a Visbrain object if you want to use the camera of an other object for the sceen rendering.

kwargs : dict | {}

Optional arguments are passed to the VisbrainCanvas class.

set_data(data, xaxis=None, yaxis=None, clim=None, cmap=None, vmin=None, under=None, vmax=None, over=None)[source][source]

Set data to the image.


Get the transform value.


Get the under value.


Get the visible_obj value.


Get the vmax value.


Get the vmin value.

Examples using visbrain.objects.ImageObj