[remove] moved correlation code from PyLoT to the seisobs utils scope
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README
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README
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PyLoT
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version: 0.1
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The Python picking and Localisation Tool
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This python library contains a graphical user interfaces for picking
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seismic phases. This software needs ObsPy (http://github.com/obspy/obspy/wiki)
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and the PySide Qt4 bindings for python to be installed first.
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PILOT has originally been developed in Mathworks' MatLab. In order to
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distribute PILOT without facing portability problems, it has been decided
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to redevelop the software package in Python. The great work of the ObsPy
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group allows easy handling of a bunch of seismic data and PyLoT will
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benefit a lot compared to the former MatLab version.
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The development of PyLoT is part of the joint research project MAGS2.
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staff:
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======
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original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
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developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff,
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C. Wollin, M. Rische
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others: A. Bruestle, T. Meier, W. Friederich
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release notes:
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==============
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October 2013
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@ -22,13 +22,18 @@ staff:
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original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
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original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
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developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff,
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developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff,
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C. Wollin, M. Rische
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C. Wollin, M. Rische, M. Paffrath
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others: A. Bruestle, T. Meier, W. Friederich
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others: A. Bruestle, T. Meier, W. Friederich
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release notes:
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release notes:
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==============
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==============
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### Features
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- consistent manual phase picking through:
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1. predefined SNR dependant zoom level
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2.
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from obspy.core import read
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from obspy.signal.trigger import coincidenceTrigger
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class CoincidenceTimes(object):
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def __init__(self, st, comp='Z', coinum=4, sta=1., lta=10., on=5., off=1.):
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_type = 'recstalta'
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self.coinclist = self.createCoincTriggerlist(data=st, trigcomp=comp,
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coinum=coinum, sta=sta,
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lta=lta, trigon=on,
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trigoff=off, type=_type)
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def __str__(self):
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n = 1
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out = ''
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for time in self.getCoincTimes():
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out += 'event no. {0}: starttime is {1}\n'.format(n, time)
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n += 1
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return out
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def getCoincTimes(self):
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timelist = []
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for info in self.getCoincList():
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timelist.append(info['time'])
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return timelist
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def getCoincList(self):
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return self.coinclist
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def createCoincTriggerlist(self, data, trigcomp, coinum, sta, lta,
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trigon, trigoff, type):
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'''
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uses a coincidence trigger to detect all events in the given
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dataset
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'''
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triggerlist = coincidenceTrigger(type, trigon, trigoff,
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data.select(component=trigcomp),
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coinum, sta=sta, lta=lta)
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return triggerlist
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def main():
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data = read('/data/SDS/2014/1A/ZV??/?H?.D/*.365')
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data.filter(type='bandpass', freqmin=5., freqmax=30.)
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coincs = CoincidenceTimes(data)
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print(coincs)
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if __name__ == '__main__':
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main()
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import numpy as np
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def crosscorrsingle(wf1, wf2, taumax):
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'''
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Calculates the crosscorrelation between two waveforms with a defined maximum timedifference.
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:param wf1: first waveformdata
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:type wf1: list
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:param wf2: second waveformdata
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:type wf2: list
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:param taumax: maximum time difference between waveforms
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:type taumax: positive integer
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:return: returns the crosscorrelation funktion 'c' and the lagvector 'l'
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:rtype: c and l are lists
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'''
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N = len(wf1)
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c = np.zeros(2 * taumax - 1)
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l = np.zeros(2 * taumax - 1)
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for tau in range(taumax):
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Cxyplus = 0
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Cxyminus = 0
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for n in range(N - tau):
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Cxy1plus = wf1[n] * wf2[n + tau]
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Cxy1minus = wf1[n + tau] * wf2[n]
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Cxyplus = Cxyplus + Cxy1plus
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Cxyminus = Cxyminus + Cxy1minus
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c[(taumax - 1) - tau] = Cxyminus
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c[(taumax - 1) + tau] = Cxyplus
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l[(taumax - 1) - tau] = -tau
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l[(taumax - 1) + tau] = tau
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return c, l
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def crosscorrnormcalc(weights, wfs):
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'''
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crosscorrnormcalc - function that calculates the normalization for the
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cross correlation carried out by 'wfscrosscorr'
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:param weights: weighting factors for the single components
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:type weights: tuple
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:param wfs: tuple of `~numpy.array` object containing waveform data
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:type wfs: tuple
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:return: a floating point number yielding the by 'weights' weighted energy
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of the waveforms in 'wfs'
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:rtype: float
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'''
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# check if the parameters are of the right type
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if not isinstance(weights, tuple):
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raise TypeError("type of 'weight' should be 'tuple', but is {0}".format(
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type(weights)))
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if not isinstance(wfs, tuple):
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raise TypeError(
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"type of parameter 'wfs' should be 'tuple', but is {0}".format(
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type(wfs)))
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sqrsumwfs = 0.
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for n, wf in enumerate(wfs):
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sqrsumwf = np.sum(weights[n] ** 2. * wf ** 2.)
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sqrsumwfs += sqrsumwf
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return np.sqrt(sqrsumwfs)
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def wfscrosscorr(weights, wfs, taumax):
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'''
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wfscrosscorr - function that calculates successive cross-correlations from a set of waveforms stored in a matrix
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base formula is:
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C(i)=SUM[p=1:nComponent](eP(p)*(SUM[n=1:N]APp(x,n)*APp(y,n+i)))/(SQRT(SUM[p=1:nComponent]eP(p)^2*(SUM[n=1:N](APp(x,n)^2)))*SQRT(SUM[p=1:nComponent]eP(p)^2*(SUM[n=1:N]APp(y,n)^2)))
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whereas
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nComponent is the number of components
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N is the number of samples
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i is the lag-index
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input:
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APp rowvectors containing the waveforms of each component p for which the cross-correlation is calculated
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tPp rowvectros containing times
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eP vector containing the weighting factors for the components (maxsize = [1x3])
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output:
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C cross-correlation function
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L lag-vector
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author(s):
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SWB 26.01.2010 as arranged with Thomas Meier and Monika Bischoff
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:param weights: weighting factors for the single components
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:type weights: tuple
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:param wfs: tuple of `~numpy.array` object containing waveform data
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:type wfs: tuple
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:param taumax: maximum time difference
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:type taumax: positive integer
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:return: returns cross correlation function normalized by the waveform energy
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'''
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ccnorm = 0.
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ccnorm = crosscorrnormcalc(weights, wfs[0])
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ccnorm *= crosscorrnormcalc(weights, wfs[1])
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c = 0.
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for n in range(len(wfs)):
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cc, l = crosscorrsingle(wfs[0][n], wfs[1][n], taumax)
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c += cc
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return c / ccnorm, l
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from obspy.signal.trigger import recursive_sta_lta, trigger_onset
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def createSingleTriggerlist(st, station='ZV01', trigcomp='Z', stalta=(1, 10),
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trigonoff=(6, 1)):
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'''
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uses a single-station trigger to create a triggerlist for this station
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:param st: obspy stream
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:type st:
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:param station: station name to get triggers for (optional, default = ZV01)
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:type station: str
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:param trigcomp: (optional, default = Z)
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:type trigcomp: str
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:param stalta: (optional, default = (1,10))
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:type stalta: tuple
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:param trigonoff: (optional, default = (6,1))
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:type trigonoff: tuple
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:return: list of triggtimes
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:rtype: list
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'''
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tr = st.copy().select(component=trigcomp, station=station)[0]
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df = tr.stats.sampling_rate
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cft = recursive_sta_lta(tr.data, int(stalta[0] * df), int(stalta[1] * df))
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triggers = trigger_onset(cft, trigonoff[0], trigonoff[1])
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trigg = []
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for time in triggers:
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trigg.append(tr.stats.starttime + time[0] / df)
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return trigg
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def createSubCoincTriggerlist(trig, station='ZV01'):
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'''
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makes a triggerlist with the events, that are triggered by the
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coincidence trigger and are seen at the demanded station
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:param trig: list containing triggers from coincidence trigger
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:type trig: list
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:param station: station name to get triggers for (optional, default = ZV01)
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:type station: str
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:return: list of triggertimes
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:rtype: list
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'''
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trigg = []
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for tri in trig:
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if station in tri['stations']:
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trigg.append(tri['time'])
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return trigg
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