import matplotlib.pyplot as plt
from tftb.generators import fmsin, fmlin, fmconst
from tftb.processing import (ideal_tfr, WignerVilleDistribution,
smoothed_pseudo_wigner_ville,
reassigned_smoothed_pseudo_wigner_ville)
sig1, if1 = fmsin(60, 0.16, 0.35, 50, 1, 0.35, 1)
sig2, if2 = fmlin(60, 0.3, 0.1)
sig3, if3 = fmconst(60, 0.4)
sig = np.hstack((sig1, np.zeros((8, )), sig2 + sig3))
iflaw = np.zeros((2, 128))
iflaw[0, :] = np.hstack((if1, np.nan * np.ones((8, )), if2))
iflaw[1, :] = np.hstack((np.nan * np.ones((68, )), if3))
tfr, t, f = ideal_tfr(iflaw)
plt.figure(figsize=(10, 8))
plt.subplot(221)
plt.contour(t, f, tfr, 1)
plt.gca().set_xticklabels([])
plt.grid(True)
plt.title("Ideal instantaneous frequencies")
plt.ylabel('Normalized Frequencies')
tfr = WignerVilleDistribution(sig).run()[0]
threshold = np.amax(np.abs(tfr)**2) * 0.05
tfr[np.abs(tfr)**2 <= threshold] = 0.0
plt.subplot(222)
plt.imshow(np.abs(tfr)**2,
extent=[0, 128, 0, 0.5],
from tftb.generators import fmsin, fmlin, fmconst
from tftb.processing.cohen import Spectrogram
from tftb.processing.reassigned import spectrogram as re_spectrogram
from tftb.processing.reassigned import morlet_scalogram as re_morlet_scalogram
from tftb.processing import ideal_tfr
sig1, if1 = fmsin(60, 0.16, 0.35, 50, 1, 0.35, 1)
sig2, if2 = fmlin(60, 0.3, 0.1)
sig3, if3 = fmconst(60, 0.4)
sig = np.hstack((sig1, np.zeros((8,)), sig2 + sig3))
iflaw = np.zeros((2, 128))
iflaw[0, :] = np.hstack((if1, np.nan * np.ones((8,)), if2))
iflaw[1, :] = np.hstack((np.nan * np.ones((68,)), if3))
tfr, t, f = ideal_tfr(iflaw)
plt.figure(figsize=(10, 8))
plt.subplot(221)
plt.contour(t, f, tfr, 1)
plt.grid(True)
plt.gca().set_xticklabels([])
plt.title("Ideal instantaneous frequencies")
plt.ylabel('Normalized Frequencies')
tfr, _, _ = Spectrogram(sig).run()
threshold = np.amax(np.abs(tfr)) * 0.05
tfr[np.abs(tfr) <= threshold] = 0.0
plt.subplot(222)
plt.imshow(np.abs(tfr)[:64, :], extent=[0, 128, 0, 0.5], aspect='auto', origin='bottomleft')
plt.grid(True)
plt.gca().set_xticklabels([])
hybrid signal (containing sinusoidal, constant and linear frequency
modulations), against its ideal time-frequency characteristics.
Figure 4.34 from the tutorial.
"""
from tftb.generators import fmsin, fmhyp
from tftb.processing import ideal_tfr, reassigned_spectrogram, Spectrogram
import numpy as np
import matplotlib.pyplot as plt
n_points = 128
sig1, if1 = fmsin(n_points, 0.15, 0.45, 100, 1, 0.4, -1)
sig2, if2 = fmhyp(n_points, [1, .5], [32, 0.05])
sig = sig1 + sig2
ideal, t, f = ideal_tfr(np.vstack((if1, if2)))
_, re_spec, _ = reassigned_spectrogram(sig)
spec, t3, f3 = Spectrogram(sig).run()
# Ideal tfr
plt.subplot(221)
plt.contour(t, f, ideal, 1)
plt.grid(True)
plt.gca().set_xticklabels([])
plt.title("Ideal time-frequency distro")
plt.ylabel('Normalized Frequency')
# Spectrogram
plt.subplot(222)
plt.contour(t3, f3[:64], spec[:64, :])
plt.grid(True)
This example compares the spectrogram and the reassigned spectrogram of a
hybrid signal (containing sinusoidal, constant and linear frequency
modulations), against its ideal time-frequency characteristics.
"""
from tftb.generators import fmsin, fmhyp
from tftb.processing import ideal_tfr, reassigned_spectrogram, Spectrogram
import numpy as np
import matplotlib.pyplot as plt
n_points = 128
sig1, if1 = fmsin(n_points, 0.15, 0.45, 100, 1, 0.4, -1)
sig2, if2 = fmhyp(n_points, [1, .5], [32, 0.05])
sig = sig1 + sig2
ideal, t, f = ideal_tfr(np.vstack((if1, if2)))
_, re_spec, _ = reassigned_spectrogram(sig)
spec, t3, f3 = Spectrogram(sig).run()
# Ideal tfr
plt.subplot(221)
plt.contour(t, f, ideal, 1)
plt.grid(True)
plt.gca().set_xticklabels([])
plt.title("Ideal time-frequency distro")
plt.ylabel('Normalized Frequency')
# Spectrogram
plt.subplot(222)
plt.contour(t3, f3[:64], spec[:64, :])
plt.grid(True)