emd.sift.ensemble_sift#

emd.sift.ensemble_sift(X, nensembles=4, ensemble_noise=0.2, noise_mode='single', noise_seed=None, nprocesses=1, sift_thresh=1e-08, max_imfs=None, verbose=None, imf_opts=None, envelope_opts=None, extrema_opts=None)[source]#

Compute Intrinsic Mode Functions with the ensemble EMD.

This function implements the ensemble empirical model decomposition algorithm defined in [1]. This approach sifts an ensemble of signals with white-noise added and treats the mean IMFs as the result. The resulting IMFs from the ensemble sift resembles a dyadic filter [2].

Parameters:

Xndarray: 1D input array containing the time-series data to be decomposed
nensemblesint: Integer number of different ensembles to compute the sift across.
ensemble_noisefloat: Standard deviation of noise to add to each ensemble (Default value = .2)
noise_mode{‘single’,’flip’}: Flag indicating whether to compute each ensemble with noise once or twice with the noise and sign-flipped noise (Default value = ‘single’)
noise_seedint: seed value to use for random noise generation.
nprocessesint: Integer number of parallel processes to compute. Each process computes a single realisation of the total ensemble (Default value = 1)
sift_threshfloat: The threshold at which the overall sifting process will stop. (Default value = 1e-8)
max_imfsint: The maximum number of IMFs to compute. (Default value = None)

Returns:

imfndarray: 2D array [samples x nimfs] containing he Intrisic Mode Functions from the decomposition of X.

Other Parameters:

imf_optsdict: Optional dictionary of keyword options to be passed to emd.get_next_imf.
envelope_optsdict: Optional dictionary of keyword options to be passed to emd.interp_envelope
extrema_optsdict: Optional dictionary of keyword options to be passed to emd.get_padded_extrema
verbose{None,’CRITICAL’,’WARNING’,’INFO’,’DEBUG’}: Option to override the EMD logger level for a call to this function.