Gts.refine_l1trend

class pyacs.gts.Gts.Gts(code=None, lat=None, lon=None, h=None, X0=None, Y0=None, Z0=None, t0=None, data=None, data_xyz=None, data_corr_neu=None, data_corr_xyz=None, offsets_dates=[], offsets_values=None, outliers=[], annual=None, semi_annual=None, velocity=None, ifile=None, log=None, metadata=None)[source]
refine_l1trend(rawts, lcomponent='ENU', min_samples_per_segment=10, threshold_bias_res_detect=10, threshold_vel=8, min_sample_segment_refinement=1, norm='L2', output='ts')

Refine results from l1trend by optimizing the date of breakpoints for suspected periods. The algorithm first identifies periods where the l1trend model might be improved. For every candidate period, the algorithm checks different metrics to decide if the model should be refined. Finally, for the selected periods, it optimizes the date of breakpoints.

Parameters:

  • rawts (pyacs.gts.Gts.Gts) – Raw time series, originally processed by l1trend

  • lcomponent (str) – Component to refine (default: ‘ENU’)

  • min_samples_per_segment (int) – Minimum number of samples for a segment to be considered as a candidate for improvement (default: 10)

  • threshold_bias_res_detect (float) – Threshold to detect bias residuals (default: 10)

  • threshold_vel (float) – Threshold to detect high velocities (default: 8)

  • min_sample_segment_refinement (int) – Minimum number of samples for a segment in the refined model (default: 1)

  • norm (str) – Norm to be minimized for improvement (default: ‘L2’)

  • output (str) – Output type ‘ts’ for the refined Gts, ‘info’ for the periods suspected, ‘both’ for both (default: ‘ts’)

Returns:

Refined Gts object or information about suspected periods

Return type:

pyacs.gts.Gts.Gts or tuple