PyWavelets/pywt

PyWavelets - Wavelet Transforms in Python

What it solves

PyWavelets provides a way to perform wavelet transforms in Python, allowing users to analyze signals or data that are localized in both time and frequency. This differs from Fourier transforms, which only provide frequency localization.

How it works

It implements various mathematical basis functions (wavelets) to perform time-frequency transforms. The library supports multiple dimensions (1D, 2D, and nD) and different types of transforms, including discrete, stationary, and continuous wavelet transforms, as well as wavelet packet decomposition.

Who it’s for

It is designed for researchers, data scientists, and engineers who need to perform signal processing and time-frequency analysis using Python.

Highlights

  • Support for 1D, 2D, and nD Forward and Inverse Discrete Wavelet Transforms (DWT and IDWT).
  • Multilevel DWT and IDWT capabilities.
  • 1D and 2D Stationary Wavelet Transforms (Undecimated Wavelet Transforms).
  • 1D and 2D Wavelet Packet decomposition and reconstruction.
  • 1D Continuous Wavelet Transform.
  • Over 100 built-in wavelet filters with support for custom wavelets.
  • Compatibility with the Matlab Wavelet Toolbox.

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