{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/3"}],"enrichment":{"capability":"PyWavelets provides discrete, continuous, and stationary wavelet transforms in 1D, 2D, and nD, along with wavelet packet decomposition and over 100 built-in wavelet filters for time-frequency analysis.","skillfed_tags":["signal-processing","time-frequency-analysis","scientific-computing"],"use_cases":["Decompose audio or seismic signals into frequency bands for feature extraction or denoising.","Analyze non-stationary signals where frequency content changes over time, such as medical data.","Perform image compression or feature detection using 2D wavelet transforms.","Implement wavelet-based filtering or signal reconstruction in processing pipelines.","Conduct time-frequency analysis for research in signal processing or engineering."],"what_it_does":"PyWavelets is a Python library for computing wavelet transforms\u2014mathematical operations that decompose signals into time-frequency components using basis functions localized in both time and frequency. Unlike Fourier transforms, which capture only frequency information, wavelet transforms preserve temporal localization, making them useful for analyzing non-stationary signals. The library supports forward and inverse discrete wavelet transforms in 1D, 2D, and nD, multilevel decomposition, stationary (undecimated) transforms, wavelet packet decomposition, continuous wavelet transforms, and custom wavelet definitions. It includes over 100 built-in wavelet filters and handles both real and complex data in single and double precision.\n\nThe package depends only on numpy and is available as precompiled wheels for modern Python versions on common platforms. It is actively maintained, marked Production/Stable, and has been in continuous development since 2006. Installation is straightforward on supported platforms via pip, though building from source requires a C compiler and Cython.","worth_installing":"Yes. PyWavelets is a mature, actively maintained library with no known vulnerabilities, permissive dual licensing (MIT and BSD-3-Clause), and stable precompiled wheels for modern Python. Install friction is moderate but manageable. It is the standard choice for wavelet transforms in Python and worth installing if your work involves time-frequency analysis, signal decomposition, or wavelet-based feature extraction."},"id":"pywavelets","links":{"html":"https://skillfed.io/packages/pywavelets","md":"https://skillfed.io/packages/pywavelets.md","pypi":"https://pypi.org/project/pywavelets/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-08-04","license_spdx":"MIT AND BSD-3-Clause","license_treatment":"permissive","name":"PyWavelets","python_support":"supports_current","summary":"PyWavelets, wavelet transform module"},"popularity":{"monthly_downloads":15254016,"position":1191,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.9.0"}