emd.sift.complete_ensemble_sift¶
- emd.sift.complete_ensemble_sift(X, nensembles=4, ensemble_noise=0.2, noise_mode='single', 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 complete ensemble EMD.
This function implements the complete ensemble empirical model decomposition algorithm defined in [1]. This approach sifts an ensemble of signals with white-noise added taking a single IMF across all ensembles at before moving to the next IMF.
- 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’)
- 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
- imf: ndarray
2D array [samples x nimfs] containing he Intrisic Mode Functions from the decomposition of X.
- noise: array_like
The 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.
See also
References
- 1
Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. https://doi.org/10.1109/icassp.2011.5947265