---

name: pywavelets description: "Routes PyWavelets users to discrete transforms, wavelet-family and CWT workflows, and wavelet-packet tree operations." disable-model-invocation: true metadata: disco-role: operating license: MIT

PyWavelets

PyWavelets provides wavelet transforms in Python through the pywt package.

Start here

• Install from PyPI: python -m pip install PyWavelets

• From this checkout: python -m pip install -e . after the source build dependencies are available.

• The current checkout's source build metadata requires Python 3.12+.

• Core runtime only needs NumPy; Matplotlib is optional for plot-heavy demo parity.

• Run python scripts/check_pywavelets_install.py from this skill directory for a no-network smoke check.

• Minimal import check:

  python - <<'PY'
  import pywt
  print(pywt.__version__)
  PY
  

• Read references/repo-provenance.md before deciding whether this skill still matches the current checkout.

• Read references/package-overview.md for the package map and bundled sample data.

• Read references/troubleshooting.md when imports, builds, transform parameters, or packet paths fail.

Route by task

• Discrete wavelet transforms, inverse transforms, multilevel coefficient trees, stationary transforms, multiresolution analysis, coefficient packing, thresholding, signal extension modes, padding, or fully separable transforms: use sub-skills/discrete-transforms/SKILL.md.
• Wavelet families, custom wavelets, wavelet functions, continuous wavelet transforms, scale/frequency conversion, or bundled demo signals/data: use sub-skills/wavelets-and-cwt/SKILL.md.
• Wavelet packet trees in 1D/2D/ND, path-based node lookup, and packet reconstruction: use sub-skills/wavelet-packets/SKILL.md.

Common defaults

• Prefer wavelet='db1' or wavelet='haar' for tiny smoke checks.
• Prefer mode='symmetric' for decimated DWT/IDWT workflows unless the task says otherwise.
• Use mode='periodization' when the workflow is explicitly SWT-based or when mra(..., transform='swt') is requested.
• Use pywt.Modes.modes to confirm supported signal extension modes.
• Use pywt.wavelist() and pywt.families() before choosing a custom wavelet family.
• Use pywt.data.camera(), pywt.data.ascent(), pywt.data.aero(), pywt.data.ecg(), or pywt.data.nino() as bundled sample inputs.

What this skill does not do

• It does not replace the focused subskills for transform, CWT, or packet workflows.
• It does not depend on the original checkout remaining present after extraction.
• It does not require plot windows, downloads, or accelerator backends for the core workflows in this repo.

Bundled helpers

• scripts/check_pywavelets_install.py checks importability and representative transform, CWT, and packet smoke paths.

Reference files

• references/package-overview.md summarizes the public package surface and source-build expectations.
• references/data-and-demo-signals.md lists bundled example arrays and demo signals that are safe for smoke checks.
• references/troubleshooting.md collects cross-cutting install, import, build, and transform failures.