visbrain.objects.TimeFrequencyObj¶

class visbrain.objects.TimeFrequencyObj(name, data=None, sf=1.0, method='fourier', nperseg=256, f_min=1.0, f_max=160.0, f_step=1.0, baseline=None, norm=None, n_window=None, overlap=0.0, window=None, c_parameter=20, 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]¶

Compute the time-frequency map (or spectrogram).

The time-frequency decomposition can be assessed using :

The fourier transform

Morlet’s wavelet

Multi-taper

Parameters:

Parameters:	name : string \| None Name of the time-frequency object. data : array_like Array of data of shape (N,) sf : float \| 1. The sampling frequency. method : {‘fourier’, ‘wavelet’, ‘multitaper’} The method to use to compute the time-frequency decomposition. nperseg : int \| 256 Length of each segment. Argument pass to the scipy.signal.spectrogram function (for ‘fourier’ and ‘multitaper’ method). overlap : float \| 0. Overlap between segments. Must be between 0. and 1. f_min : float \| 1. Minimum frequency (for ‘wavelet’ method). f_max : float \| 160. Maximum frequency (for ‘wavelet’ method). f_step : float \| 2. Frequency step between two consecutive frequencies (for ‘wavelet’ method). baseline : array_like \| None Baseline period (for ‘wavelet’ method). norm : int \| None The normalization type (for ‘wavelet’ method).. See the normalization function. n_window : int \| None If this parameter is an integer, the time-frequency map is going to be averaged into smaller windows (for ‘wavelet’ method). window : {‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’} Windowing method for averaging. By default, ‘flat’ is used for Wavelet and ‘hamming’ for Fourier. c_parameter : int \| 20 Parameter ‘c’ described in doi:10.1155/2011/980805 (for ‘multitaper’ method) 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. over : 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`).

name : string | None: Name of the time-frequency object.
data : array_like: Array of data of shape (N,)
sf : float | 1.: The sampling frequency.
method : {‘fourier’, ‘wavelet’, ‘multitaper’}: The method to use to compute the time-frequency decomposition.
nperseg : int | 256: Length of each segment. Argument pass to the scipy.signal.spectrogram function (for ‘fourier’ and ‘multitaper’ method).
overlap : float | 0.: Overlap between segments. Must be between 0. and 1.
f_min : float | 1.: Minimum frequency (for ‘wavelet’ method).
f_max : float | 160.: Maximum frequency (for ‘wavelet’ method).
f_step : float | 2.: Frequency step between two consecutive frequencies (for ‘wavelet’ method).
baseline : array_like | None: Baseline period (for ‘wavelet’ method).
norm : int | None: The normalization type (for ‘wavelet’ method).. See the normalization function.
n_window : int | None: If this parameter is an integer, the time-frequency map is going to be averaged into smaller windows (for ‘wavelet’ method).
window : {‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’}: Windowing method for averaging. By default, ‘flat’ is used for Wavelet and ‘hamming’ for Fourier.
c_parameter : int | 20: Parameter ‘c’ described in doi:10.1155/2011/980805 (for ‘multitaper’ method)
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.
over : 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).

Notes

List of supported shortcuts :

s : save the figure

<delete> : reset camera

Examples

>>> import numpy as np
>>> from visbrain.objects import TimeFrequencyObj
>>> n, sf = 512, 256  # number of time-points and sampling frequency
>>> time = np.arange(n) / sf  # time vector
>>> data = np.sin(2 * np.pi * 25. * time) + np.random.rand(n)
>>> tf = TimeFrequencyObj('tf', data, sf)
>>> tf.preview(axis=True)

Methods

`__init__`(name[, data, sf, method, nperseg, …])	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[, sf, method, nperseg, f_min, …])	Compute TF and set data to the ImageObj.
`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.

clim¶: Get the clim value.

cmap¶: Get the cmap value.

interpolation¶: Get the interpolation value.

name¶: Get the name value.

over¶: Get the over value.

parent¶: Get the parent value.

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

Previsualize the result.

Parameters:

Parameters:	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.

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.

Parameters:

Parameters:	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.

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, sf=1.0, method='fourier', nperseg=256, f_min=1.0, f_max=160.0, f_step=1.0, baseline=None, norm=None, n_window=None, overlap=0.0, window=None, c_parameter=20, clim=None, cmap='viridis', vmin=None, under=None, vmax=None, over=None)[source][source]¶: Compute TF and set data to the ImageObj.

transform¶: Get the transform value.

under¶: Get the under value.

visible_obj¶: Get the visible_obj value.

vmax¶: Get the vmax value.

vmin¶: Get the vmin value.

Examples using `visbrain.objects.TimeFrequencyObj`¶

Image, time-frequency map and spectrogram objects

visbrain.objects.TimeFrequencyObj¶

Examples using visbrain.objects.TimeFrequencyObj¶

Examples using `visbrain.objects.TimeFrequencyObj`¶