pyacs.gts.lib.offset module¶
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pyacs.gts.lib.offset.suspect_offsets_mf(self, threshold=3, verbose=True, lcomponent='NE', n_max_offsets=5, in_place=False, debug=False)[source]¶ Tries to find offsets in a time series using a median filter
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pyacs.gts.lib.offset.test_offset_significance(self, date, conf_level=95, lcomponent='NE', verbose=True, debug=False, mode='local')[source]¶ test the significance of an offset
- Param
date : date of the offset to be tested
- Param
conf_level : confidence level in percent (default=95)
- Param
lcomponent : component to be tested (default=’NE’)
- Param
mode : choose among ‘local’,’detrend’,’detrend_seasonal’ to test significance
- Param
verbose : verbose mode
- Returns
: True if significant, else False
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pyacs.gts.lib.offset.local_offset_robust(self, date, n, verbose=False, debug=False)[source]¶ estimate a local offset (no velocity) with a robust method
- Parameters
date – date in decimal year
n – number of dates before and after the dates used in the estimation
:return : a 1D numpy array with [date, north, east, up, s_north, s_east, s_up]
- Note
the offsets are estimated using the difference between the median position before and after the earthquake using i days
for all i <=n. Then the median of the estimates is returned.
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pyacs.gts.lib.offset.apply_offsets(self, np_offset, opposite=False, in_place=False, verbose=False)[source]¶ Applies given offsets to a times series np_offset is a 1D np.array with lines [dates,north,east,up] if in_place = True then replace the current time series
- Parameters
np_offset – 1D or 3D numpy array or list or list of list with column offset_dates, north, east, up, s_north, s_east, s_up
opposite – boolean, if True apply the oppsite of provided offsets
in_place – if True, will make change in place, if False, return s a new time series
verbose – boolean, verbose mode
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pyacs.gts.lib.offset.find_offsets_t_scan(self, threshold=0.8, window=250, in_place=False, lcomponent='NE', verbose=True, debug=True)[source]¶
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pyacs.gts.lib.offset.get_suspected_dates(diff_data, threshold, lcomponent='NEU', verbose=False)[source]¶ get the list of the largest values; these are suspected offsets
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pyacs.gts.lib.offset.check_suspected_offsets(lindex, verbose=False)[source]¶ Check that the list of suspected index does not contain two successive values. In this case, it is certainly an isolated outlier and the suspected offsets are removed from the list.
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pyacs.gts.lib.offset.find_offsets(self, threshold=3, n_max_offsets=9, conf_level=95, lcomponent='NE', verbose=True, in_place=False)[source]¶ A simple empirical procedure to find offsets.
- Parameters
threshold – threshold value for offset preliminary detection
n_max_offset – maximum number of offsets to be detected simultaneously
conf_level – confidence level for a suspected offset to be accepted
lcomponent – components used for offset detection
- Returns
a new Gts instance with offsets_dates and outliers now populated
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pyacs.gts.lib.offset.suspect_offsets(self, threshold=3, verbose=True, lcomponent='NE', n_max_offsets=10, in_place=False)[source]¶ Tries to find offsets in a time series
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pyacs.gts.lib.offset.find_time_offsets(self, option=None, ndays=7, th_detection_rms=3, th_detection_offset=3)[source]¶ Find the time of suspected offsets by rms time series calculated over ndays Then check the time of offsets: if one offset is too small/None then it is removed input:
ndays: number of positions in the windows. rms time series are calculated over ndays.
th_detection_rms: the threshold for detecting the anomalous windows rms(t) > th_detection_rms*median(rms(ts)).
- th_detection_offset: the threshold for detecting the offsets,
for each anomalous time windows, differentiate positions then test whether it is a suspected offset (differentiated(t) > threshold * median(differentiated))
output:
add the time of offsets in to self.offsets
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pyacs.gts.lib.offset.delete_small_offsets(self, offsets, del_by_pricise=False)[source]¶ The aim for test_offset modul. Estimate the offsets with clean data. Then delete the offsets which their values are so small input: list of time offsets output: list of time offsets tested