superlets

QhX.algorithms.superlets.superlets.superlets_methods(tt, mag, ntau, minfq=10, maxfq=500)[source]

Perform a hybrid 2D method using superlets on time-series data.

This function applies the superlet transform to the provided time-series data and then computes a correlation matrix using the resulting transformed data. It’s particularly useful for time-frequency analysis with high resolution.

Parameters:

  • tt (-) – Array of time data points.

  • mag (-) – Array of magnitude values corresponding to the time data.

  • ntau (-) – Number of time divisions for the analysis. Controls the resolution in time.

  • minfq (-) – Minimum frequency of interest. Default is 10.

  • -maxfq (float, optional) – Maximum frequency of interest. Default is 500.

Returns:

  • -ndarray – Correlation matrix derived from the superlet transform of the input data.

  • -extent (list) – Extent of the correlation matrix values, given as [min, max, min, max].

Examples

>>> tt = np.linspace(0, 10, 1000)
>>> mag = np.sin(tt)
>>> corr, extent = superlets_methods(tt, mag, 100)