class visbrain.objects.ConnectObj(name, nodes, edges, select=None, line_width=3.0, color_by='strength', custom_colors=None, alpha=1.0, antialias=False, dynamic=None, cmap='viridis', clim=None, vmin=None, vmax=None, under='gray', over='red', transform=None, parent=None, verbose=None, _z=-10.0, **kw)[source][source]

Create a connectivity object.

name : string

The name of the connectivity object.

nodes : array_like

Array of nodes coordinates of shape (n_nodes, 3).

edges : array_like | None

Array of ponderations for edges of shape (n_nodes, n_nodes).

select : array_like | None

Array to select edges to display. This should be an array of boolean values of shape (n_nodes, n_nodes).

line_width : float | 3.

Connectivity line width.

color_by : {‘strength’, ‘count’}

Coloring method. Use ‘strength’ to color edges according to their connection strength define by the edges input. Use ‘count’ to color edges according to the number of connections per node.

custom_colors : dict | None

Use a dictionary to colorize edges. For example, {1.2: ‘red’, 2.8: ‘green’, None: ‘black’} turn connections that have a 1.2 and 2.8 strength into red and green. All others connections are set to black.

alpha : float | 1.

Transparency level (if dynamic is None).

antialias : bool | False

Use smoothed lines.

dynamic : tuple | None

Control the dynamic opacity. For example, if dynamic=(0, 1), strong connections will be more opaque than weaker connections.

cmap : string | ‘viridis’

Colormap to use if custom_colors is None.

vmin : float | None

Lower threshold of the colormap if custom_colors is None.

under : string | None

Color to use for values under vmin if custom_colors is None.

vmin : float | None

Higher threshold of the colormap if custom_colors is None.

over : string | None

Color to use for values over vmax if custom_colors is None.

transform : VisPy.visuals.transforms | None

VisPy transformation to set to the parent node.

parent : VisPy.parent | None

Line object parent.

verbose : string

Verbosity level.

_z : float | 10.

In case of (n_sources, 2) use _z to specify the elevation.

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 ConnectObj
>>> n_nodes = 100
>>> nodes = np.random.rand(n_nodes, 3)
>>> edges = np.random.uniform(low=-10., high=10., size=(n_nodes, n_nodes))
>>> select = np.logical_and(edges >= 0, edges <= 1.)
>>> c = ConnectObj('Connect', nodes, edges, select=select, cmap='inferno',
>>>                antialias=True)
>>> c.preview(axis=True)


__init__(name, nodes, edges[, select, …]) 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_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() Update the line.
update_from_dict(kwargs) Update attributes from a dictionary.

Get the alpha value.


Get the cmap value.


Get the color_by value.


Get the dynamic value.


Get the line_width value.


Get the name 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.


Get the transform value.


Get the visible_obj value.