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name: wavelets-and-cwt description: "Routes PyWavelets users through wavelet-family inspection, custom wavelets, wavefun output, scale conversion, and continuous wavelet transforms." disable-model-invocation: true metadata: disco-role: operating license: MIT

Wavelets and CWT

Use this sub-skill when the task is about the wavelet catalog, custom wavelet construction, wavelet-function inspection, or the continuous wavelet transform.

Route here when the task asks for

• families() or wavelist()
• Wavelet, ContinuousWavelet, or DiscreteContinuousWavelet
• custom wavelets built from a filter_bank
• wavefun, integrate_wavelet, central_frequency, scale2frequency, or frequency2scale
• cwt and continuous-wavelet scaleograms
• bundled demo signals such as pywt.data.demo_signal(...) or the sample pywt.data.nino() time series

Route elsewhere when the task is about

• decimated transforms, SWT, MRA, coefficient packing, thresholding, padding, or fully separable transforms: go to ../discrete-transforms/SKILL.md
• packet trees, packet path traversal, or packet reconstruction: go to ../wavelet-packets/SKILL.md

Start here

• Read references/api-reference.md for the verified constructor signatures and the live cwt call shape.
• Read references/workflows.md for recipes that choose a wavelet, inspect wavefun, and compute a CWT.
• Read references/troubleshooting.md when wavelet names, continuous/dtype selection, or CWT scale/method choices fail.
• Run ../../scripts/inspect_wavelet.py when you want a safe, no-plot inspection of a discrete or continuous wavelet.
• Run ../../scripts/check_pywavelets_install.py when you want a broader smoke check that also exercises the core transform stack.

Common workflow anchors

• Use pywt.families() to group the built-in discrete wavelet families.
• Use pywt.wavelist(kind='continuous') to choose a continuous wavelet for cwt.
• Use pywt.DiscreteContinuousWavelet(name) when you need a one-call resolver for discrete or continuous names.
• Use pywt.ContinuousWavelet(name, dtype) when you want to force a continuous wavelet object directly.
• Use pywt.scale2frequency(...) and pywt.frequency2scale(...) to convert between CWT scale and normalized frequency.
• Use pywt.data.nino() or pywt.data.demo_signal('Doppler', n) for a small real-valued CWT input.

Important package facts

• The live package accepts continuous wavelets such as morl, mexh, cmor, shan, fbsp, and cgau for cwt.
• The live cwt implementation requires a continuous wavelet object and positive scales.
• The code path currently accepts method='conv' and method='fft'; do not rely on auto as a runtime option.
• wavefun returns 2, 3, or 5 outputs depending on whether the wavelet is continuous, orthogonal, or biorthogonal.

What to expect from the references

• references/api-reference.md records the verified public constructors, helper functions, and cwt arguments.
• references/workflows.md shows how to inspect wavelet objects and compute a scaleogram without guessing parameters.
• references/troubleshooting.md covers the common continuous-wavelet, dtype, parameter-string, and scale errors.