Currently running this example code of mac ios 10.14.3 with anaconda with python 3.6
The code runs without any flaws , however there is no output that is displayed at the end.
Is there some settings that i am missing out
Code used:
`"""
Image, time-frequency map and spectrogram objects
Use and control image, time-frequency maps and spectrogram.
* Display and configure an image (color, interpolation)
* Compute and display time-frequency properties of a signal (spectrogram,
wavelet based time-frequency maps or multi-taper)
"""
import numpy as np
from visbrain.objects import (ImageObj, TimeFrequencyObj, ColorbarObj,
SceneObj)
###############################################################################
Scene creation
###############################################################################
First, we define the scene and a few colorbar properties (like font size,
colorbar width...)
CBAR_STATE = dict(cbtxtsz=12, txtsz=10., width=.1, rect=(-0.2, -2., 1., 4.),
cbtxtsh=4.)
sc = SceneObj(size=(1000, 600))
###############################################################################
Create sample data
###############################################################################
Then we create some data for 1) images (a basic diagonale image) and 2) a
sine signal with a main frequency at 25hz
Define a (10, 10) image
n = 10
image = np.r_[np.arange(n - 1), np.arange(n)[::-1]]
image = image.reshape(-1, 1) + image.reshape(1, -1)
image[np.diag_indices_from(image)] = 30.
Define a 25hz sine
n, sf = 512, 256
time = np.arange(n) / sf # time vector
data = np.sin(2 * np.pi * 25. * time) + np.random.rand(n)
###############################################################################
Plot an image
###############################################################################
Most basic plot of the image without further customization
im_basic = ImageObj('ex1', image)
sc.add_to_subplot(im_basic, row=0, col=0, title='Basic image', zoom=.9)
###############################################################################
Interpolated image
###############################################################################
The image can also be interpolated. Checkout the complete list on the
VisPy website (vispy.visuals.ImageVisual)
im_interp = ImageObj('ex2', image, interpolation='bicubic')
sc.add_to_subplot(im_interp, row=0, col=1, title='Interpolated image', zoom=.9)
###############################################################################
Color properties
###############################################################################
The ImageObj allow several custom color properties (such as color
thresholding, colormap control...)
Create the image object
im_color = ImageObj('ex3', image, interpolation='bilinear', cmap='Spectral_r',
vmin=5., vmax=20., under='gray', over='darkred')
sc.add_to_subplot(im_color, row=0, col=2, title='Custom colors', zoom=.9)
Get the colorbar of the image
cb_im_color = ColorbarObj(im_color, cblabel='Image data', **CBAR_STATE)
sc.add_to_subplot(cb_im_color, row=0, col=3, width_max=150, zoom=.9)
###############################################################################
Spectrogram
###############################################################################
Extract time-frequency properties using the Fourier transform
spec = TimeFrequencyObj('spec', data, sf, method='fourier', cmap='RdBu_r')
sc.add_to_subplot(spec, row=1, col=0, title='Spectrogram', zoom=.9)
###############################################################################
Time-frequency map
###############################################################################
Extract time-frequency properties using the wavelet convolution
tf = TimeFrequencyObj('tf', data, sf, method='wavelet')
sc.add_to_subplot(tf, row=1, col=1, title='Time-frequency map', zoom=.9)
###############################################################################
Multi-taper
###############################################################################
Extract time-frequency properties using multi-taper (need installation of
lspopt package)
tf_mt = TimeFrequencyObj('mt', data, sf, method='multitaper', overlap=.7,
interpolation='bicubic', cmap='Spectral_r')
sc.add_to_subplot(tf_mt, row=1, col=2, title='Multi-taper', zoom=.9)
cb_tf_win = ColorbarObj(tf_mt, cblabel='Power', **CBAR_STATE)
sc.add_to_subplot(cb_tf_win, row=1, col=3, width_max=150, zoom=.9)
Display the scene
sc.preview()
`
output of the script
Creation of a scene ImageObj(name='ex1') created ImageObj(name='ex1') added to the scene ImageObj(name='ex2') created ImageObj(name='ex2') added to the scene ImageObj(name='ex3') created ImageObj(name='ex3') added to the scene Get colorbar properties from ImageObj(name='ex3') object ColorbarObj(name='ex3Cbar') created ColorbarObj(name='ex3Cbar') added to the scene TimeFrequencyObj(name='spec') created Compute time-frequency decomposition using the fourier method TimeFrequencyObj(name='spec') added to the scene TimeFrequencyObj(name='tf') created Compute time-frequency decomposition using the wavelet method Compute the time-frequency map (normalization=None) TimeFrequencyObj(name='tf') added to the scene TimeFrequencyObj(name='mt') created Compute time-frequency decomposition using the multitaper method TimeFrequencyObj(name='mt') added to the scene Get colorbar properties from TimeFrequencyObj(name='mt') object ColorbarObj(name='mtCbar') created ColorbarObj(name='mtCbar') added to the scene
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