###################################################################
## detrend_annual
###################################################################
[docs]
def detrend_annual(self, method='L2', periods=None, exclude_periods=None):
"""
Estimate trend + annual terms and remove them from the time series.
Velocity and annual are saved in Gts.velocity and Gts.annual.
Parameters
----------
method : str, optional
Estimation method (e.g. 'L2').
periods : list, optional
Periods used for estimation.
exclude_periods : list, optional
Periods to exclude from estimation.
Returns
-------
Gts
Detrended time series (new instance).
Notes
-----
Outliers (Gts.outliers) are omitted in the estimation; offsets (Gts.offsets_dates)
are estimated simultaneously.
"""
import numpy as np
from pyacs.gts.Gts import Gts
import inspect
import logging
import pyacs.message.message as MESSAGE
import pyacs.message.verbose_message as VERBOSE
import pyacs.message.error as ERROR
import pyacs.message.warning as WARNING
import pyacs.message.debug_message as DEBUG
# after this method .data and .data_xyz are not consistent so .data_xyz is set to None
#self.data_xyz = None
###########################################################################
# check data is not None
from pyacs.gts.lib.errors import GtsInputDataNone
try:
if self.data is None:
# raise exception
raise GtsInputDataNone(inspect.stack()[0][3], __name__, self)
except GtsInputDataNone as error:
# print PYACS WARNING
ERROR(error)
return (self)
###########################################################################
import copy
outliers = copy.deepcopy(self.outliers)
tmp_ts = self.remove_outliers()
if periods:
tmp_ts = tmp_ts.extract_periods(periods)
if exclude_periods:
tmp_ts = tmp_ts.exclude_periods(periods)
detrended = tmp_ts.make_model(option='detrend_annual', method=method)
vel = detrended.velocity
offsets_values = detrended.offsets_values
annual = detrended.annual
new_gts = self.copy()
new_gts.outliers = outliers
new_gts.offsets_values = offsets_values
new_gts.velocity = vel
new_gts.annual = annual
model = new_gts.mmodel()
new_gts.data[:, 1:4] = new_gts.data[:, 1:4] - model.data[:, 1:4]
return (new_gts)