Continuous Wavelet Transform (CWT)

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Continuous Wavelet Transform (CWT)#

ptwt.cwt(data: Tensor, scales: ndarray | Tensor, wavelet: ContinuousWavelet | str, sampling_period: float = 1.0, precision: int = 12) → tuple[Tensor, ndarray][source]#

Compute the single-dimensional continuous wavelet transform.

This function is a PyTorch port of pywt.cwt as found at: PyWavelets/pywt

Parameters:

  • data (torch.Tensor) – The input tensor of shape [batch_size, time].

  • scales (torch.Tensor or np.array) – The wavelet scales to use. One can use f = pywt.scale2frequency(wavelet, scale)/sampling_period to determine what physical frequency, f. Here, f is in hertz when the sampling_period is given in seconds.

  • wavelet (ContinuousWavelet or str) – The continuous wavelet to work with.

  • sampling_period (float) – Sampling period for the frequencies output (optional). The values computed for coefs are independent of the choice of sampling_period (i.e. scales is not scaled by the sampling period).

  • precision (int) – Length of the wavelet used for the CWT.

Raises:

ValueError – If a scale is too small for the input signal.

Returns:

A tuple (out_tensor, frequencies). The first tuple-element contains the transformation matrix of shape [scales, batch, time]. The second element contains an array with frequency information.

Example

>>> import torch, ptwt
>>> import numpy as np
>>> import scipy.signal as signal
>>> t = np.linspace(-2, 2, 800, endpoint=False)
>>> sig = signal.chirp(t, f0=1, f1=12, t1=2, method="linear")
>>> widths = np.arange(1, 31)
>>> cwtmatr, freqs = ptwt.cwt(
>>>     torch.from_numpy(sig), widths, "mexh", sampling_period=(4 / 800) * np.pi
>>> )
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