points = np.arange(-min([lg, signal.shape[0] - ti - tau]),
min([lg, ti - 1 - tau]) + 1)
g2 = twindow[lg + points]
g2 = g2 / np.sum(g2)
R = np.sum(g2 * signal[ti + tau - points - 1] * np.conj(signal[ti - tau - points - 1]))
tfr[1 + tau, icol] = fwindow[lh + tau + 1] * R
R = np.sum(g2 * signal[ti - tau - points - 1] * np.conj(signal[ti + tau - points - 1]))
tfr[freq_bins - tau - 1, icol] = fwindow[lh - tau + 1] * R
tau = np.round(freq_bins / 2.0)
if (ti <= signal.shape[0] - tau) and (ti >= tau + 1) and (tau <= lh):
points = np.arange(-min([lg, signal.shape[0] - ti - tau]),
min([lg, ti - 1 - tau]) + 1)
g2 = twindow[lg + 1 + points]
g2 = g2 / np.sum(g2)
_x = np.sum(g2 * signal[ti + tau - points] * np.conj(signal[ti - tau - points]))
_x *= fwindow[lh + tau + 1]
_y = np.sum(g2 * signal[ti - tau - points] * np.conj(signal[ti + tau - points]))
_y *= fwindow[lh - tau + 1]
tfr[tau, icol] = (_x + _y) * 0.5
tfr = np.fft.fft(tfr, axis=0)
return np.real(tfr)
if __name__ == '__main__':
from tftb.generators import anapulse
sig = anapulse(128)
t = np.linspace(0, 1, 128)
spec = WignerVilleDistribution(sig, timestamps=t)
spec.run()
spec.plot(kind="contour", scale="log")
g2 = g2 / np.sum(g2)
R = np.sum(g2 * signal[ti + tau - points - 1] *
np.conj(signal[ti - tau - points - 1]))
tfr[1 + tau, icol] = fwindow[lh + tau + 1] * R
R = np.sum(g2 * signal[ti - tau - points - 1] *
np.conj(signal[ti + tau - points - 1]))
tfr[freq_bins - tau - 1, icol] = fwindow[lh - tau + 1] * R
tau = np.round(freq_bins / 2.0)
if (ti <= signal.shape[0] - tau) and (ti >= tau + 1) and (tau <= lh):
points = np.arange(-min([lg, signal.shape[0] - ti - tau]),
min([lg, ti - 1 - tau]) + 1)
g2 = twindow[lg + 1 + points]
g2 = g2 / np.sum(g2)
_x = np.sum(g2 * signal[ti + tau - points] *
np.conj(signal[ti - tau - points]))
_x *= fwindow[lh + tau + 1]
_y = np.sum(g2 * signal[ti - tau - points] *
np.conj(signal[ti + tau - points]))
_y *= fwindow[lh - tau + 1]
tfr[tau, icol] = (_x + _y) * 0.5
tfr = np.fft.fft(tfr, axis=0)
return np.real(tfr)
if __name__ == '__main__':
from tftb.generators import anapulse
sig = anapulse(128)
spec = WignerVilleDistribution(sig)
spec.run()
spec.plot(kind="contour", scale="log")
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2015 jaidev <jaidev@newton>
#
# Distributed under terms of the MIT license.
"""
"""
from tftb.generators import anapulse
import matplotlib.pyplot as plt
import numpy as np
x = 2.5 * anapulse(512, 301)
plt.plot(np.real(x))
plt.xlim(0, 512)
plt.grid()
plt.title('Analytic Dirac Impulse')
plt.show()
===================================
This example plots the scalogram of a Dirac impulse functions. This shows the
behaviour of the scalograms as the scale (or inversely, the frequency) changes.
it is well localized for small scales (large frequencies), and less localized
as the scale increases (as the frequency decreases).
Figure 3.19 from the tutorial.
"""
from tftb.generators import anapulse
from tftb.processing import Scalogram
import numpy as np
import matplotlib.pyplot as plt
sig1 = anapulse(128)
tfr, t, f, _ = Scalogram(sig1,
waveparams=6,
fmin=0.05,
fmax=0.45,
n_voices=128).run()
tfr = np.abs(tfr)**2
threshold = np.amax(tfr) * 0.05
tfr[tfr <= threshold] = 0.0
t, f = np.meshgrid(t, f)
plt.contour(t, f, tfr, 20)
plt.grid()
plt.title('Morlet Scalogram of a Dirac Impluse')
plt.xlabel('Time')
plt.ylabel('Normalized Frequency')
plt.show()
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2015 jaidev <jaidev@newton>
#
# Distributed under terms of the MIT license.
"""
Example showing Morlet scalogram of a Dirac impulse.
"""
from tftb.generators import anapulse
from tftb.processing import scalogram
import numpy as np
import matplotlib.pyplot as plt
sig1 = anapulse(128)
tfr, t, f, _ = scalogram(sig1, waveparams=6, fmin=0.05, fmax=0.45, n_voices=128)
tfr = np.abs(tfr) ** 2
threshold = np.amax(tfr) * 0.05
tfr[tfr <= threshold] = 0.0
t, f = np.meshgrid(t, f)
plt.contour(t, f, tfr, 20)
plt.grid()
plt.title('Morlet Scalogram of a Dirac Impluse')
plt.xlabel('Time')
plt.ylabel('Normalized Frequency')
plt.show()
