spkit.simplify_signal

spkit.simplify_signal(x, fs, winlen, overlap, N=1, mag=-1, thr=-20, minDur=0.01, freq_devOffset=10, freq_devSlope=0.1, window='blackmanharris')

Simplify a signal with Sinasodal Model

Simplify a signal with Sinasodal Decomposition-Recomposition Model

Reconstructing a signal from captured sinusodal tracks from Sinasodal Model sineModel_analysis

Parameters:
x: 1d array

  • input signal

fs: int, scalar

  • sampling frequency

winlen: int

  • analysis window length

overlap: int,

  • analysis overlap length

N: int, default=1,

  • number of sinusodal tracks to retain

  • higher the number, more complexity is captured

thr: scalar, default=-20,

  • threshold on magnitude spectum, in dB

  • lower the value, more values are retained

mag: scalar, default=-1

  • to enforce the sinusodal wave,

  • if mag=0 or 1, signal is recostructed from sinusodal tracks, ignoring the original magnitude of track

  • in case of single track (N=1), recostructed signal will be much like sine wave of varying frequecy but not varying amplitude.

Other: default

  • other parameters are from sineModel_analysis, which have their default values sets

  • (minDur=0.01,freq_devOffset=10,freq_devSlope=0.1,window=’blackmanharris’)

Returns:
xr: 1d-array

  • reconstructed signal

(fXst, mXst, pXst): output of sineModel_analysis

See also

dft_analysis

Discreet Fourier Transform - DFT

dft_synthesis

Inverse Discreet Fourier Transform - iDFT

stft_analysis

Short-Time Fourier Transform - STFT

stft_synthesis

Inverse Short-Time Fourier Transform - iSTFT

frft

Fractional Frourier Transform - FRFT

ifrft

Inverse Fractional Frourier Transform - iFRFT

sineModel_analysis

Sinasodal Model Decomposition

sineModel_synthesis

Sinasodal Model Synthesis

Notes

#TODO

References

  • wikipedia -

Examples

#sp.simplify_signal
import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
x,fs,lead_names = sp.data.ecg_sample_12leads(sample=2)
x = x[:int(fs*10),5]
x = sp.filterDC_sGolay(x, window_length=fs//3+1)
t = np.arange(len(x))/fs
N = 1
y, (fXst, mXst, pXst) =  sp.simplify_signal(x,fs,winlen=201,overlap=50,mag=-1,N=N,thr=-50,minDur=0.01,
                                            freq_devOffset=10,freq_devSlope=0.1,window='blackmanharris')
plt.figure(figsize=(10,4))
plt.subplot(211)
plt.plot(t,x)
plt.xlim([t[0],t[-1]])
plt.title('x: signal')
plt.subplot(212)
plt.plot(t,x,alpha=0.3)
plt.plot(t,y[:len(t)])
plt.xlim([t[0],t[-1]])
plt.title('y: simplified signal')
plt.tight_layout()
plt.show()
../../_images/spkit-simplify_signal-1.png