Quickstart

Quickstart#

Install#

pip install pywavelet

Minimal round-trip (time → wavelet → time)#

The WDM transform operates on a regular grid with:

  • Nf frequency bins

  • Nt time bins

  • ND = Nf * Nt samples in the 1D input signal

import numpy as np
from scipy.signal import chirp

from pywavelet.types import TimeSeries
from pywavelet.transforms import from_time_to_wavelet, from_wavelet_to_time

dt = 1 / 512
Nf = 64
Nt = 64
mult = 16
ND = Nf * Nt

t = np.arange(ND) * dt
y = chirp(t, f0=10.0, f1=100.0, t1=t[-1], method="hyperbolic")

h_time = TimeSeries(data=y, time=t)
h_wavelet = from_time_to_wavelet(h_time, Nf=Nf, Nt=Nt, mult=mult)
h_recon = from_wavelet_to_time(h_wavelet, dt=dt, mult=mult)

# Quick visualization helpers
_ = h_time.plot_spectrogram()
_ = h_wavelet.plot(absolute=True, cmap="Reds")

Notes#

  • If your input length is not exactly Nf * Nt, pad/crop the data before calling the transform.

  • The transform backend (NumPy/JAX/CuPy) is chosen at import time; see Backends & Precision if you want GPU/JAX support.

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