pyacs.gts.lib.l1trend.check_trend module
Functions for checking and analyzing L1-trend model quality.
- pyacs.gts.lib.l1trend.check_trend.check_l1_trend(ts, l1ts, component='ENU', min_samples_per_segment=4, threshold_bias_res_detect=40, threshold_vel=8, plot=False)[source]
Inspect the result from l1trend model of a time series. Returns a list periods ([sdate,edate]) where bad modelling is suspected.
- Parameters:
ts (pyacs.gts.Gts.Gts) – Raw time series
l1ts (pyacs.gts.Gts.Gts) – L1-trend time series
component (str) – Components to be analyzed. Default: ‘EN’
min_samples_per_segment (int) – Minimum number of samples for a segment to be inspected (default: 6)
threshold_bias_res_detect (float) – Threshold to detect bias residuals (default: 40)
threshold_vel (float) – Instantaneous velocity threshold for a segment to be considered in detection (default: 8 mm/yr)
plot (bool) – Plot the results (default: False)
- Returns:
(H_period, H_cp, H_cp_pb) - H_period: Dictionary of suspicious periods for each component - H_cp: Dictionary of breakpoint indices for each component - H_cp_pb: Dictionary of problematic breakpoint indices for each component
- Return type:
tuple
- pyacs.gts.lib.l1trend.check_trend.compute_measure_bias(t, l1y, y, cp, min_samples_per_segment=6, threshold_bias_res_detect=40, threshold_vel=8, verbose=True)[source]
Compute fit indicators from residual (obs minus piecewise linear).
- Parameters:
t (ndarray) – Time array.
l1y (ndarray) – L1-trend values.
y (ndarray) – Residual (obs - piecewise_linear).
cp (ndarray) – Breakpoint indices.
min_samples_per_segment (int, optional) – Minimum samples per segment (default 6).
threshold_bias_res_detect (float, optional) – Threshold for bias detection (default 40).
threshold_vel (float, optional) – Velocity threshold in mm/yr (default 8).
verbose (bool, optional) – Verbose mode.
- Returns:
(list of suspicious periods [sdate, edate], list of problematic breakpoint indices).
- Return type:
tuple
- pyacs.gts.lib.l1trend.check_trend.make_node_ts(x, y, threshold=0.5)[source]
Find breakpoints assuming y is piecewise linear.
- Parameters:
x (ndarray) – Time/abscissa.
y (ndarray) – Values (piecewise linear).
threshold (float, optional) – Threshold on change in slope to declare breakpoint.
- Returns:
Indices of breakpoints in the time series.
- Return type:
ndarray