pywavelets

library1.9.0pypypi✓ verified 27d ago

PyWavelets is a free, open-source Python library for wavelet transforms. It provides 1D, 2D, and nD forward and inverse Discrete Wavelet Transforms (DWT and IDWT), Stationary Wavelet Transforms (SWT), Wavelet Packet decomposition, and Continuous Wavelet Transforms (CWT). It combines a simple high-level interface with low-level C and Cython performance and is widely used in signal processing, image compression, and noise removal. The current version is 1.9.0 and it has an active release cadence, with minor updates and new features being released regularly.

data ai-ml

pip install PyWavelets

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · PYWAVELETS

pywavelets

datapythonv1.9.0

Install

3.9s avg

Import

303ms

Disk

98MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.8.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.310s · 98.3MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 3.9s · import 0.296s · 95MB

98MB installed

● package 98MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

pywt

✓ import pywt

This quickstart demonstrates a basic 1D Discrete Wavelet Transform (DWT) and its inverse using the 'db1' (Daubechies 1) wavelet. It shows how to decompose a signal into approximation (cA) and detail (cD) coefficients, and then reconstruct the original signal.

import pywt import numpy as np data = np.array([1, 2, 3, 4, 5, 6]) wavelet = 'db1' # Perform a single-level Discrete Wavelet Transform (DWT) cA, cD = pywt.dwt(data, wavelet) print(f"Original data: {data}") print(f"Approximation coefficients (cA): {cA}") print(f"Detail coefficients (cD): {cD}") # Perform inverse DWT to reconstruct the signal reconstructed_data = pywt.idwt(cA, cD, wavelet) print(f"Reconstructed data: {reconstructed_data}")

Debug

Known issues

breakingPyWavelets 1.9.0 dropped support for Python 3.10 and added support for Python 3.14. Ensure your Python environment meets the minimum requirement of Python >=3.11.

fix

Upgrade Python to 3.11 or newer, or downgrade PyWavelets to a compatible version if unable to upgrade Python.

affects: 1.9.0+

breakingThe minimum supported NumPy version has been updated. PyWavelets 1.9.0 explicitly requires NumPy >= 1.25.0. Using an older NumPy version will likely lead to installation or runtime errors.

fix

Update NumPy to version 1.25.0 or higher using `pip install --upgrade numpy`.

affects: 1.9.0+

breakingThe optional dependency on SciPy for FFT operations was removed in PyWavelets 1.9.0. If your application implicitly relied on SciPy being available due to PyWavelets, it might now be missing.

fix

Manually install `scipy` (e.g., `pip install scipy`) if your application or specific PyWavelets functions (like certain CWT functionalities) require it.

affects: 1.9.0+

gotchaThe image returned by `pywt.data.camera` was replaced in version 1.2.0 with a new CC0-licensed image due to licensing restrictions on the original 'cameraman' image. If your application relies on the exact pixel data of the original image, it will be different in newer versions.

fix

For commercial use, the new image is safe. If the original image is critical for non-commercial use, you must source it externally or use an older PyWavelets version.

affects: 1.2.0+

gotchaThe length of coefficient arrays returned by `pywt.dwt` depends on the chosen signal extension `mode`. For all modes except 'periodization' ('per'), `len(cA) == len(cD) == floor((len(data) + wavelet.dec_len - 1) / 2)`. For 'periodization' mode, `len(cA) == len(cD) == ceil(len(data) / 2)`. This can lead to unexpected output sizes.

fix

Always explicitly specify the `mode` parameter (e.g., `mode='symmetric'`) and consult the documentation or `pywt.dwt_coeff_len()` to predict output sizes, especially when chaining transforms or performing inverse transforms.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'pywt'

The PyWavelets library is not installed in the active Python environment or is installed in a different environment.

fix

pip install PyWavelets

AttributeError: module 'pywt' has no attribute 'dwt_2d'

The function name for a 2D Discrete Wavelet Transform is `dwt2`, not `dwt_2d`.

fix

import pywt

coeffs = pywt.dwt2(data, wavelet_name)

ValueError: Invalid wavelet name 'my_wavelet'. Use pywt.wavelist() to see valid wavelet names.

The specified wavelet name does not correspond to any of the wavelets available in the PyWavelets library.

fix

import pywt

# To see available wavelets:
print(pywt.wavelist())

# Use a valid wavelet name, e.g.
coeffs = pywt.dwt(data, 'db1')

TypeError: expected an array of at least 1 dimension (got 0)

PyWavelets functions expect array-like input, but a scalar value was provided instead, leading to a NumPy dimension error.

fix

import numpy as np
import pywt

data = np.array([1, 2, 3, 4])  # Ensure data is an array
coeffs = pywt.dwt(data, 'db1')

Upgrade

Version history

1.9.0latest on PyPI · released Aug 4, 2025

Audit

Dependencies

numpyrequiredRequired for numerical array operations. PyWavelets 1.9.0 requires NumPy >= 1.25.0.

matplotliboptionalOptional dependency, required for running tests and many examples involving plotting.

Agent activity

14 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/PyWavelets/pywt ↗

packagehttps://pypi.org/project/pywavelets/ ↗

docshttps://pywavelets.readthedocs.io/ ↗