class visbrain.objects.TimeSeries3DObj(name, data, xyz, select=None, line_width=1.5, color='white', ts_amp=6.0, ts_width=20.0, alpha=1.0, antialias=False, translate=(0.0, 0.0, 1.0), transform=None, parent=None, verbose=None, _z=-10.0, **kw)[source][source]

Create a 3-D time-series object.

name : string

Name of the time-series object.

data : array_like

Array of time-series of shape (n_sources, n_time_points)

xyz : array_like

The 3-D center location of each time-series of shape (n_sources, 3).

select : array_like | None

Select the time-series to display. Should be a vector of bolean values of shape (n_sources,).

line_width : float | 1.5

Time-series’ line width.

color : array_like/tuple/string | ‘white’

Time-series’ color.

ts_amp : float | 6.

Graphical amplitude of the time-series.

ts_width : float | 20.

Graphical width of the time-series.

alpha : float | 1.

Time-series transparency.

antialias : bool | False

Use smooth lines.

translate : tuple | (0., 0., 1.)

Translate the time-series over the (x, y, z) axes.

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 TimeSeries3DObj
>>> n_pts, n_ts = 100, 5
>>> time = np.arange(n_pts)
>>> phy = np.random.uniform(2, 30, (n_ts))
>>> data = np.sin(2 * np.pi * time.reshape(1, -1) * phy.reshape(-1, 1))
>>> xyz = np.random.uniform(-20, 20, (n_ts, 3))
>>> ts = TimeSeries3DObj('Ts', data, xyz, antialias=True, color='red',
>>>                    line_width=3.)
>>> ts.preview(axis=True)


__init__(name, data, xyz[, select, …]) Init.
animate([step, interval, iterations]) Animate the object.
copy() Get a copy of the object.
describe_tree() Tree description.
preview([bgcolor, axis, xyz, show, obj, …]) Previsualize the result.
record_animation(name[, n_pic, bgcolor]) Record an animated object and save as a *.gif file.
render() Render the canvas.
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 line.
update_from_dict(kwargs) Update attributes from a dictionary.

Get the alpha value.

animate(step=1.0, interval='auto', iterations=-1)[source]

Animate the object.

Note that this method can only be used with 3D objects.

step : float | 1.

Rotation step.

interval : float | ‘auto’

Time between events in seconds. The default is ‘auto’, which attempts to find the interval that matches the refresh rate of the current monitor. Currently this is simply 1/60.

iterations : int | -1

Number of iterations. Can be -1 for infinite.


Get the cmap value.


Get the color value.


Get a copy of the object.


Get the data_folder 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), mpl=False, **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.

mpl : bool | False

Use Matplotlib to display the object. This result in a non interactive figure.

kwargs : dict | {}

Optional arguments are passed to the VisbrainCanvas class.

record_animation(name, n_pic=10, bgcolor=None)[source]

Record an animated object and save as a *.gif file.

Note that this method :

  • Can only be used with 3D objects.
  • Requires the python package imageio
name : string

Name of the gif file (e.g ‘myfile.gif’)

n_pic : int | 10

Number of pictures to use to render the gif.

bgcolor : string, tuple, list | None

Background color.


Render the canvas.

img : array_like

Array of shape (n_rows, n_columns, 4) where 4 describes the RGBA components.

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 translate value.


Get the ts_amp value.


Get the ts_width value.


Get the visible_obj value.