Synchrosqueezing is a powerful reassignment method that focuses time-frequency representations, and allows extraction of instantaneous amplitudes and frequencies. Friendly overview.
- Continuous Wavelet Transform (CWT), forward & inverse, and its Synchrosqueezing
- Short-Time Fourier Transform (STFT), forward & inverse, and its Synchrosqueezing
- Wavelet visualizations and testing suite
- Generalized Morse Wavelets
- Ridge extraction
- Speed: fastest wavelet transforms in Python1, beating MATLAB
1: feel free to open Issue showing otherwise
pip install ssqueezepy
. Or, for latest version (most likely stable):
pip install git+https://github.com/OverLordGoldDragon/ssqueezepy
Multi-threaded execution is enabled by default (disable via os.environ['SSQ_PARALLEL'] = '0'
). GPU requires CuPy >= 8.0.0
and PyTorch >= 1.8.0 installed (enable via os.environ['SSQ_GPU'] = '1'
). pyfftw
optionally supported for maximum CPU FFT speed.
See Performance guide.
ssqueezepy
is equipped with a visualization toolkit, useful for exploring wavelet behavior across scales and configurations. (Also see explanations and code)
import numpy as np
import matplotlib.pyplot as plt
from ssqueezepy import ssq_cwt, ssq_stft
def viz(x, Tx, Wx):
plt.imshow(np.abs(Wx), aspect='auto', cmap='jet')
plt.show()
plt.imshow(np.flipud(np.abs(Tx)), aspect='auto', vmin=0, vmax=.2, cmap='jet')
plt.show()
#%%# Define signal ####################################
N = 2048
t = np.linspace(0, 10, N, endpoint=False)
xo = np.cos(2 * np.pi * 2 * (np.exp(t / 2.2) - 1))
xo += xo[::-1] # add self reflected
x = xo + np.sqrt(2) * np.random.randn(N) # add noise
plt.plot(xo); plt.show()
plt.plot(x); plt.show()
#%%# CWT + SSQ CWT ####################################
Twxo, Wxo, *_ = ssq_cwt(xo)
viz(xo, Twxo, Wxo)
Twx, Wx, *_ = ssq_cwt(x)
viz(x, Twx, Wx)
#%%# STFT + SSQ STFT ##################################
Tsxo, Sxo, *_ = ssq_stft(xo)
viz(xo, Tsxo, np.flipud(Sxo))
Tsx, Sx, *_ = ssq_stft(x)
viz(x, Tsx, np.flipud(Sx))
Also see ridge extraction README.
- Continuous Wavelet Transform, & vs STFT
- Synchrosqueezing's phase transform, intuitively
- Wavelet time & frequency resolution visuals
- Why oscillations in SSQ of mixed sines? Separability visuals
- Zero-padding's effect on spectrum
DSP fundamentals: I recommend starting with 3b1b's Fourier Transform, then proceeding with DSP Guide chapters 7-11. The Discrete Fourier Transform lays the foundation of signal processing with real data. Deeper on DFT coefficients here, also 3b1b.
- David Bondesson: ridge extraction (
ridge_extraction.py
;examples/
:extracting_ridges.py
,ridge_extraction/README.md
)
ssqueezepy
was originally ported from MATLAB's Synchrosqueezing Toolbox, authored by E. Brevdo and G. Thakur [1]. Synchrosqueezed Wavelet Transform was introduced by I. Daubechies and S. Maes [2], which was followed-up in [3], and adapted to STFT in [4]. Many implementation details draw from [5]. Ridge extraction based on [6].
- G. Thakur, E. Brevdo, N.-S. Fučkar, and H.-T. Wu. "The Synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications", Signal Processing 93:1079-1094, 2013.
- I. Daubechies, S. Maes. "A Nonlinear squeezing of the Continuous Wavelet Transform Based on Auditory Nerve Models".
- I. Daubechies, J. Lu, H.T. Wu. "Synchrosqueezed Wavelet Transforms: a Tool for Empirical Mode Decomposition", Applied and Computational Harmonic Analysis 30(2):243-261, 2011.
- G. Thakur, H.T. Wu. "Synchrosqueezing-based Recovery of Instantaneous Frequency from Nonuniform Samples", SIAM Journal on Mathematical Analysis, 43(5):2078-2095, 2011.
- Mallat, S. "Wavelet Tour of Signal Processing 3rd ed".
- D. Iatsenko, P. V. E. McClintock, A. Stefanovska. "On the extraction of instantaneous frequencies from ridges in time-frequency representations of signals".
ssqueezepy is MIT licensed, as found in the LICENSE file. Some source functions may be under other authorship/licenses; see NOTICE.txt.