get_outliers#
- pymultifracs.robust.get_outliers(wt_coefs, scaling_ranges, pelt_beta, threshold, pelt_jump=1, robust_cm=False, verbose=False, generalized=False, remove_edges=False)#
Detect outliers in a signal.
- Parameters:
- wt_coefs
WaveletDec Input coefficients of the signal with outliers.
- scaling_ranges
list[tuple[int,int]] List of pairs of (j1, j2) ranges of scales for the linear regressions.
- pelt_beta
float Regularization parameter for the PELT segmentation.
- threshold
float Wasserstein distance threshold to indentify a segment as outlier.
- pelt_jump
int Optional, PELT algorithm checks segmentations every pelt_jump point.
- robust_cmbool
Whether to use robust cumulants in the detection.
- generalizedbool
Whether to use the exponential power distribution model instead of the normal distribution for the log 1-leaders in the detection.
- verbosebool, optional
Display figures outlining the detection process. If multiple signals are being processed, will only show figures for the first signal.
- wt_coefs
- Returns:
- leaders
WaveletLeader Wavelet 1-leaders used in the analysis.
- idx_reject
dict[int,ndarray] Dictionary associating to each scale the boolean mask of indices to reject.
- leaders
See also
mfa()Can be fed the output dictionary:
idx_reject.