visbrain.objects.TimeSeries3DObj¶

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.

Parameters

namestring: Name of the time-series object.
dataarray_like: Array of time-series of shape (n_sources, n_time_points)
xyzarray_like: The 3-D center location of each time-series of shape (n_sources, 3).
selectarray_like | None: Select the time-series to display. Should be a vector of bolean values of shape (n_sources,).
line_widthfloat | 1.5: Time-series’ line width.
colorarray_like/tuple/string | ‘white’: Time-series’ color.
ts_ampfloat | 6.: Graphical amplitude of the time-series.
ts_widthfloat | 20.: Graphical width of the time-series.
alphafloat | 1.: Time-series transparency.
antialiasbool | False: Use smooth lines.
translatetuple | (0., 0., 1.): Translate the time-series over the (x, y, z) axes.
transformVisPy.visuals.transforms | None: VisPy transformation to set to the parent node.
parentVisPy.parent | None: Line object parent.
verbosestring: Verbosity level.
_zfloat | 10.: In case of (n_sources, 2) use _z to specify the elevation.
kwdict | {}: Optional arguments are used to control the colorbar (See ColorbarObj).

Notes

List of supported shortcuts :

s : save the figure

<delete> : reset camera

Examples

>>> 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)

Methods

`__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.

alpha¶: 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.

Parameters

stepfloat | 1.: Rotation step.
intervalfloat | ‘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.
iterationsint | -1: Number of iterations. Can be -1 for infinite.

cmap¶: Get the cmap value.

color¶: Get the color value.

copy()[source]¶: Get a copy of the object.

data_folder¶: Get the data_folder value.

line_width¶: Get the line_width value.

name¶: Get the name value.

parent¶: 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.

Parameters

bgcolorarray_like/string/tuple | ‘black’: Background color for the preview.
axisbool | False: Add x and y axis with ticks.
xyzbool | False: Add an (x, y, z) axis to the scene.
objVisbrainObj | None: Pass a Visbrain object if you want to use the camera of an other object.
sizetuple | (1200, 800): Default size of the window.
mplbool | False: Use Matplotlib to display the object. This result in a non interactive figure.
kwargsdict | {}: 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

Parameters

namestring: Name of the gif file (e.g ‘myfile.gif’)
n_picint | 10: Number of pictures to use to render the gif.
bgcolorstring, tuple, list | None: Background color.

render()[source]¶

Render the canvas.

Returns

imgarray_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.

Parameters

saveasstr: The name of the file to be saved. This file must contains a extension like .png, .tiff, .jpg…
print_sizetuple | 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.
dpifloat | 300.: Dots per inch for printing the image.
unit{‘centimeter’, ‘millimeter’, ‘pixel’, ‘inch’}: Unit of the printed size.
factorfloat | None: If you don’t want to use the print_size input, factor simply multiply the resolution of your screen.
regiontuple | None: Select a specific region. Must be a tuple of four integers each one describing (x_start, y_start, width, height).
autocropbool | False: Automaticaly crop the figure in order to have the smallest space between the brain and the border of the picture.
bgcolorarray_like/string | None: The background color of the image.
transparentbool | False: Specify if the exported figure have to contains a transparent background.
objVisbrainObj | None: Pass a Visbrain object if you want to use the camera of an other object for the sceen rendering.
kwargsdict | {}: Optional arguments are passed to the VisbrainCanvas class.

transform¶: Get the transform value.

translate¶: Get the translate value.

ts_amp¶: Get the ts_amp value.

ts_width¶: Get the ts_width value.

visible_obj¶: Get the visible_obj value.

Examples using `visbrain.objects.TimeSeries3DObj`¶

Combine multiple objects¶

Time-series and pictures 3D (TimeSeries3DObj & Picture3DObj): complete tutorial¶

Add time series¶

visbrain.objects.TimeSeries3DObj¶

Examples using visbrain.objects.TimeSeries3DObj¶

Examples using `visbrain.objects.TimeSeries3DObj`¶