wavelet_analysis#

pymultifracs.wavelet_analysis(signal, wt_name='db3', j2=None, normalization=1)#

Compute wavelet coefficient and wavelet leaders.

Parameters:

signalndarray of float, shape (n_samples,) | (n_samples, n_realisations): Time series to analyze.
wt_namestr: Name of the wavelet function to use, as defined in the pywavelet package [1]. The default value of 'db3' means Daubechies with 3 vanishing moments.
j2int | None: Upper bound of the scale range for which wavelet coefficients will be computed. If None, it will automatically be set to the highest value possible.
normalizationint: Norm to use on the wavelet coefficients, see notes for more details.

Returns:

Notes

When computing the wavelet coefficients, the values corrupted by border effects are set to NaN (np.nan).

This makes it easier to compute the wavelet leaders, since corrupted values will also be nan and can be easily discarded.

Note

Wavelet coefficients are usually L^1 normalized [2], which is achieved by setting normalization=1.

References

[1]

[2]