API Reference#

Multifractal Analysis#

pymultifracs.mfa:

mf_analysis(mrq, scaling_ranges[, weighted, ...])

Perform multifractal analysis, given wavelet coefficients.

Used to compute and store intermediary results

`multiresquantity.MultiResolutionQuantity`(...)	Handles multi-resolution quantities in multifractal analysis.
`cumulants.Cumulants`(mrq, n_cumul, ...)	Computes and analyzes cumulants
`structurefunction.StructureFunction`(mrq, q, ...)	Computes and analyzes structure functions
`mfspectrum.MultifractalSpectrum`(mrq, ...[, ...])	Estimates the Multifractal Spectrum

Storing the multifractal analysis output

utils.MFractalVar(structure, cumulants, ...)

Aggregates the output of multifractal analysis

pymultifracs.wavelet:

`decomposition_level`(length, wt_name)	Checks the maximum scale which can be used to decompose a signal of given length
`wavelet_analysis`(signal[, p_exp, wt_name, ...])	Compute wavelet coefficient and wavelet leaders.

WaveletTransform(wt_coefs, wt_leaders, ...)

Aggregates the output of wavelet analysis

pymultifracs.simul:

`mrw`(shape, H, lam, L[, sigma, method, z0])	Create a realization of fractional Brownian motion using circulant matrix embedding.
`fbm`(args, *kwargs)

pymultifracs.psd:

`plot_psd`(signal, fs[, n_fft, seg_size, ...])	Plot the superposition of Fourier-based Welch estimation and Wavelet-based estimation of PSD on a log-log graphic.
`wavelet_estimation`(signal, fs, n_moments[, ...])	PSD estimation based on Wavelet coefficients
`welch_estimation`(signal, fs[, n_fft, seg_size])	Wrapper for `scipy.signal.welch`

PSD(freq, psd)

Aggregates power spectral density information

pymultifracs.utils:

`scale2freq`(scale, sfreq)
`freq2scale`(freq, sfreq)