###################################################################
# UPDATE STS
###################################################################
if replace:
MESSAGE("reading original time series")
try:
ots = Sgts(ts_dir=path_pck, read=True, verbose=verbose)
except:
ots = Sgts(ts_dir=path_pos, read=True, verbose=verbose)
MESSAGE("%d time series read" % ots.n())
MESSAGE("merging original and new time series")
i = 1
for code in sorted(sts.lcode()):
MESSAGE("merging time series for %s %04d / %04d " %
(code, i, len(sts.lcode())))
# insert the created ts
if ots.has_ts(code):
ots.__dict__[code] = ots.__dict__[code].insert_ts(
sts.__dict__[code], rounding='hour', data='xyz', overlap=True)
# calculates .data from .data_xyz
ots.__dict__[code].xyz2neu(corr=True)
i = i + 1
sts = ots.copy()
else:
MESSAGE("reordering date in times series")
sts.gts('reorder')
# TESTS FOR gts subpackage
from pyacs.gts.Sgts import Sgts as Sgts
import numpy as np
from pyacs.gts.lib.outliers import find_outliers_sliding_window
dir_test = 'data/ts'
dir_output = 'output'
# Reading ts files
ts = Sgts(dir_test)
# select site
code = ts.lcode()[0]
# test plot basic
ts.__dict__[code].plot(save=dir_output + '/' + code + '_01.png',
verbose=True,
title='ex_01: raw time series')
# test outliers percentage
ts.__dict__[code]\
.find_outliers_percentage()\
.plot(save=dir_output+'/'+code+'_02.png',verbose=True, title='ex_02: outliers (percentage method)')
# test outliers smoothing time windows
ts.__dict__[code]\
.find_outliers_sliding_window()\
.plot(save=dir_output+'/'+code+'_03.png',verbose=True, title='ex_03: outliers (sliding window method)')