pymultifracs.structurefunction.StructureFunction#

class pymultifracs.structurefunction.StructureFunction(mrq: dataclasses.InitVar[MultiResolutionQuantity], q: array, scaling_ranges: List[Tuple[int]], weighted: str = None, bootstrapped_mfa: dataclasses.InitVar[MFractalVar] = None)#

Computes and analyzes structure functions

Parameters:

mrqMultiResolutionQuantity: Multi resolution quantity to analyze.
qndarray, shape (n_exponents,): Exponent for which to compute the structure function
j1int: Lower-bound of the scale support for the linear regressions.
j2int: Upper-bound of the scale support for the linear regressions.
weighted: str | None: Whether to used weighted linear regressions.

Attributes:

formalismstr: Formalism used. Can be any of ‘wavelet coefs’, ‘wavelet leaders’, or ‘wavelet p-leaders’.
njdict(ndarray): Number of coefficients at scale j. Arrays are of the shape (n_rep,)
jndarray, shape (n_scales,): List of the j values (scales), in order presented in the value arrays.
j1int: Lower-bound of the scale support for the linear regressions.
j2int: Upper-bound of the scale support for the linear regressions.
weightedstr | None: Whether weighted regression was performed.
qndarray, shape (n_exponents,): Exponents for which the structure functions have been computed
valuesndarray, shape (n_exponents, n_scales, n_rep): Structure functions : \(S(j, q)\)
logvaluesndarray, shape (n_exponents, n_scales, n_rep): \(\log_2 S(j, q)\)
zetandarray, shape(n_exponents, n_rep): Scaling function : \(\zeta(q)\)
Hndarray, shape (n_rep,) | None: Estimates of H. Set to None if 2 is not in q.
n_repint: Number of realisations

classmethod from_dict(d)#

Method to instanciate a dataclass by passing a dictionary with extra keywords

Parameters:

ddict: Dictionary containing at least all the parameters required by __init__, but can also contain other parameters, which will be ignored

Returns:

MultiResolutionQuantityBase: Properly initialized multi resolution quantity

Notes

Note

Normally, dataclasses can only be instantiated by only specifiying parameters expected by the automatically generated __init__ method. Using this method instead allows us to discard extraneous parameters, similarly to introducing a **kwargs parameter.

get_nj()#: Returns nj as a list

get_nj_interv(j1, j2)#: Returns nj as an array, for j in [j1,j2]

plot(figlabel='Structure Functions', nrow=4, filename=None, ignore_q0=True, figsize=None, scaling_range=0, plot_scales=None, plot_CI=True)#: Plots the structure functions.