Removed class EarlLatePicker, replaced by new function earllatepicker.
This commit is contained in:
Ludger Küperkoch
parent
16ae4bdfe9
commit
787cac7d68
@ -27,15 +27,11 @@ class AutoPicking(object):
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Superclass of different, automated picking algorithms applied on a CF determined
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Superclass of different, automated picking algorithms applied on a CF determined
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using AIC, HOS, or AR prediction.
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using AIC, HOS, or AR prediction.
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'''
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'''
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def __init__(self, cf, nfac, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None):
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def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None):
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'''
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'''
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:param: cf, characteristic function, on which the picking algorithm is applied
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:param: cf, characteristic function, on which the picking algorithm is applied
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:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
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:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
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:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
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in EarlLatePicker
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:type: int
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:param: TSNR, length of time windows around pick used to determine SNR [s]
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:param: TSNR, length of time windows around pick used to determine SNR [s]
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:type: tuple (T_noise, T_gap, T_signal)
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:type: tuple (T_noise, T_gap, T_signal)
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@ -63,7 +59,6 @@ class AutoPicking(object):
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self.Tcf = cf.getTimeArray()
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self.Tcf = cf.getTimeArray()
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self.Data = cf.getXCF()
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self.Data = cf.getXCF()
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self.dt = cf.getIncrement()
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self.dt = cf.getIncrement()
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self.setnfac(nfac)
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self.setTSNR(TSNR)
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self.setTSNR(TSNR)
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self.setPickWindow(PickWindow)
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self.setPickWindow(PickWindow)
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self.setiplot(iplot)
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self.setiplot(iplot)
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@ -74,25 +69,18 @@ class AutoPicking(object):
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def __str__(self):
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def __str__(self):
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return '''\n\t{name} object:\n
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return '''\n\t{name} object:\n
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nfac:\t{nfac}\n
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TSNR:\t\t\t{TSNR}\n
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TSNR:\t\t\t{TSNR}\n
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PickWindow:\t{PickWindow}\n
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PickWindow:\t{PickWindow}\n
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aus:\t{aus}\n
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aus:\t{aus}\n
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Tsmooth:\t{Tsmooth}\n
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Tsmooth:\t{Tsmooth}\n
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Pick1:\t{Pick1}\n
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Pick1:\t{Pick1}\n
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'''.format(name=type(self).__name__,
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'''.format(name=type(self).__name__,
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nfac=self.getnfac(),
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TSNR=self.getTSNR(),
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TSNR=self.getTSNR(),
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PickWindow=self.getPickWindow(),
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PickWindow=self.getPickWindow(),
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aus=self.getaus(),
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aus=self.getaus(),
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Tsmooth=self.getTsmooth(),
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Tsmooth=self.getTsmooth(),
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Pick1=self.getpick1())
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Pick1=self.getpick1())
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def getnfac(self):
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return self.nfac
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def setnfac(self, nfac):
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self.nfac = nfac
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def getTSNR(self):
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def getTSNR(self):
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return self.TSNR
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return self.TSNR
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@ -127,15 +115,6 @@ class AutoPicking(object):
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def getSlope(self):
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def getSlope(self):
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return self.slope
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return self.slope
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def getLpick(self):
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return self.LPick
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def getEpick(self):
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return self.EPick
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def getPickError(self):
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return self.PickError
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def getiplot(self):
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def getiplot(self):
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return self.iplot
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return self.iplot
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@ -165,7 +144,6 @@ class AICPicker(AutoPicking):
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print 'AICPicker: Get initial onset time (pick) from AIC-CF ...'
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print 'AICPicker: Get initial onset time (pick) from AIC-CF ...'
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self.Pick = None
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self.Pick = None
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self.PickError = None
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#find NaN's
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#find NaN's
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nn = np.isnan(self.cf)
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nn = np.isnan(self.cf)
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if len(nn) > 1:
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if len(nn) > 1:
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@ -263,7 +241,7 @@ class AICPicker(AutoPicking):
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p1, = plt.plot(self.Tcf, x / max(x), 'k')
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p1, = plt.plot(self.Tcf, x / max(x), 'k')
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p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
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p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
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if self.Pick is not None:
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if self.Pick is not None:
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p3, = plt.plot([self.Pick, self.Pick], [-1 , 1], 'b', linewidth=2)
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p3, = plt.plot([self.Pick, self.Pick], [-0.1 , 0.5], 'b', linewidth=2)
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plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
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plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
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else:
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else:
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plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
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plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
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@ -281,7 +259,7 @@ class AICPicker(AutoPicking):
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p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
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p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
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plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], \
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plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], \
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loc='best')
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loc='best')
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plt.title('SNR and Slope, Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \
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plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \
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self.SNR, self.slope))
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self.SNR, self.slope))
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.ylabel('Counts')
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plt.ylabel('Counts')
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@ -307,7 +285,6 @@ class PragPicker(AutoPicking):
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print 'PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
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print 'PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
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self.Pick = None
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self.Pick = None
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self.PickError = None
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self.SNR = None
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self.SNR = None
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self.slope = None
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self.slope = None
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#smooth CF
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#smooth CF
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@ -392,313 +369,3 @@ class PragPicker(AutoPicking):
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self.Pick = None
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self.Pick = None
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print 'PragPicker: No initial onset time given! Check input!'
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print 'PragPicker: No initial onset time given! Check input!'
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return
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return
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class EarlLatePicker(AutoPicking):
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'''
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Method to derive earliest and latest possible pick after Diehl & Kissling (2009)
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as reasonable uncertainties. Latest possible pick is based on noise level,
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earliest possible pick is half a signal wavelength in front of most likely
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pick given by PragPicker. Most likely pick (initial pick) must be given.
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'''
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def calcPick(self):
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self.LPick = None
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self.EPick = None
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self.PickError = None
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self.SNR = None
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self.slope = None
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if self.getpick1() is not None:
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print 'EarlLatePicker: Get earliest and latest possible pick relative to most likely pick ...'
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ti = self.getpick1()
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x = self.Data
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t = self.Tcf
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#some parameters needed:
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tnoise = self.TSNR[0] #noise window length for calculating noise level
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tsignal = self.TSNR[2] #signal window length
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tsafety = self.TSNR[1] #safety gap between signal onset and noise window
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#get latest possible pick
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#get noise window
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inoise = np.where((self.Tcf <= max([ti - tsafety, 0])) \
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& (self.Tcf >= max([ti - tnoise - tsafety, 0])))
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#get signal window
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isignal = np.where((self.Tcf <= min([ti + tsignal + tsafety, len(x[0].data)])) \
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& (self.Tcf >= ti))
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#calculate noise level
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if len(x) == 1:
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nlevel = max(abs(x[0].data[inoise])) * self.nfac
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#get time where signal exceeds nlevel
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ilup = np.where(x[0].data[isignal] > nlevel)
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ildown = np.where(x[0].data[isignal] < -nlevel)
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if len(ilup[0]) <= 1 and len(ildown[0]) <= 1:
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print 'EarlLatePicker: Signal lower than noise level, misspick?'
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return
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il = min([ilup[0][0], ildown[0][0]])
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self.LPick = t[isignal][il]
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elif len(x) == 2:
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nlevel = max(np.sqrt(np.power(x[0].data[inoise], 2) + np.power(x[1].data[inoise], 2)))
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#get earliest time where signal exceeds nlevel
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ilup1 = np.where(x[0].data[isignal] > nlevel)
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ilup2 = np.where(x[1].data[isignal] > nlevel)
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ildown1 = np.where(x[0].data[isignal] < -nlevel)
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ildown2 = np.where(x[1].data[isignal] < -nlevel)
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if np.size(ilup1) < 1 and np.size(ilup2) > 1:
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ilup = ilup2
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elif np.size(ilup1) > 1 and np.size(ilup2) < 1:
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ilup = ilup1
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elif np.size(ilup1) < 1 and np.size(ilup2) < 1:
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ilup = None
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else:
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ilup = min([ilup1[0][0], ilup2[0][0]])
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if np.size(ildown1) < 1 and np.size(ildown2) > 1:
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ildown = ildown2
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elif np.size(ildown1) > 1 and np.size(ildown2) < 1:
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ildown = ildown1
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elif np.size(ildown1) < 1 and np.size(ildown2) < 1:
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ildown = None
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else:
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ildown = min([ildown1[0][0], ildown2[0][0]])
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if ilup == None and ildown == None:
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print 'EarlLatePicker: Signal lower than noise level, misspick?'
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return
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il = min([ilup, ildown])
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self.LPick = t[isignal][il]
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elif len(x) == 3:
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nlevel = max(np.sqrt(np.power(x[0].data[inoise], 2) + np.power(x[1].data[inoise], 2) + \
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np.power(x[2].data[inoise], 2)))
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#get earliest time where signal exceeds nlevel
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ilup1 = np.where(x[0].data[isignal] > nlevel)
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ilup2 = np.where(x[1].data[isignal] > nlevel)
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ilup3 = np.where(x[2].data[isignal] > nlevel)
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ildown1 = np.where(x[0].data[isignal] < -nlevel)
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ildown2 = np.where(x[1].data[isignal] < -nlevel)
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ildown3 = np.where(x[2].data[isignal] < -nlevel)
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if np.size(ilup1) > 1 and np.size(ilup2) < 1 and np.size(ilup3) < 1:
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ilup = ilup1
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elif np.size(ilup1) > 1 and np.size(ilup2) > 1 and np.size(ilup3) < 1:
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ilup = min([ilup1[0][0], ilup2[0][0]])
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elif np.size(ilup1) > 1 and np.size(ilup2) > 1 and np.size(ilup3) > 1:
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ilup = min([ilup1[0][0], ilup2[0][0], ilup3[0][0]])
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elif np.size(ilup1) < 1 and np.size(ilup2) > 1 and np.size(ilup3) > 1:
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ilup = min([ilup2[0][0], ilup3[0][0]])
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elif np.size(ilup1) > 1 and np.size(ilup2) < 1 and np.size(ilup3) > 1:
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ilup = min([ilup1[0][0], ilup3[0][0]])
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elif np.size(ilup1) < 1 and np.size(ilup2) < 1 and np.size(ilup3) < 1:
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ilup = None
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else:
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ilup = min([ilup1[0][0], ilup2[0][0], ilup3[0][0]])
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if np.size(ildown1) > 1 and np.size(ildown2) < 1 and np.size(ildown3) < 1:
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ildown = ildown1
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elif np.size(ildown1) > 1 and np.size(ildown2) > 1 and np.size(ildown3) < 1:
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ildown = min([ildown1[0][0], ildown2[0][0]])
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elif np.size(ildown1) > 1 and np.size(ildown2) > 1 and np.size(ildown3) > 1:
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ildown = min([ildown1[0][0], ildown2[0][0], ildown3[0][0]])
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elif np.size(ildown1) < 1 and np.size(ildown2) > 1 and np.size(ildown3) > 1:
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ildown = min([ildown2[0][0], ildown3[0][0]])
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elif np.size(ildown1) > 1 and np.size(ildown2) < 1 and np.size(ildown3) > 1:
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ildown = min([ildown1[0][0], ildown3[0][0]])
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elif np.size(ildown1) < 1 and np.size(ildown2) < 1 and np.size(ildown3) < 1:
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ildown = None
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else:
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ildown = min([ildown1[0][0], ildown2[0][0], ildown3[0][0]])
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if ilup == None and ildown == None:
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print 'EarlLatePicker: Signal lower than noise level, misspick?'
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return
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il = min([ilup, ildown])
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self.LPick = t[isignal][il]
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#get earliest possible pick
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#get next 2 zero crossings after most likely pick
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#if there is one trace in stream
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if len(x) == 1:
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zc = []
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zc.append(ti)
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i = 0
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for j in range(isignal[0][1],isignal[0][len(t[isignal]) - 1]):
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i = i+ 1
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if x[0].data[j-1] <= 0 and x[0].data[j] >= 0:
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zc.append(t[isignal][i])
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elif x[0].data[j-1] > 0 and x[0].data[j] <= 0:
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zc.append(t[isignal][i])
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if len(zc) == 3:
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break
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#calculate maximum period of signal out of zero crossings
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Ts = max(np.diff(zc))
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#if there are two traces in stream
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#get maximum of two signal periods
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if len(x) == 2:
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zc1 = []
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zc2 = []
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zc1.append(ti)
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zc2.append(ti)
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i = 0
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for j in range(isignal[0][1],isignal[0][len(t[isignal]) - 1]):
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i = i+ 1
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if x[0].data[j-1] <= 0 and x[0].data[j] >= 0:
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zc1.append(t[isignal][i])
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elif x[0].data[j-1] > 0 and x[0].data[j] <= 0:
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zc1.append(t[isignal][i])
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if x[1].data[j-1] <= 0 and x[1].data[j] >= 0:
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zc2.append(t[isignal][i])
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elif x[1].data[j-1] > 0 and x[1].data[j] <= 0:
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zc2.append(t[isignal][i])
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if len(zc1) >= 3 and len(zc2) >= 3:
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break
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Ts = max([max(np.diff(zc1)), max(np.diff(zc2))])
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#if there are three traces in stream
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#get maximum of three signal periods
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if len(x) == 3:
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zc1 = []
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zc2 = []
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zc3 = []
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zc1.append(ti)
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zc2.append(ti)
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zc3.append(ti)
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i = 0
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for j in range(isignal[0][1],isignal[0][len(t[isignal]) - 1]):
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i = i+ 1
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if x[0].data[j-1] <= 0 and x[0].data[j] >= 0:
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zc1.append(t[isignal][i])
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elif x[0].data[j-1] > 0 and x[0].data[j] <= 0:
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zc1.append(t[isignal][i])
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if x[1].data[j-1] <= 0 and x[1].data[j] >= 0:
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zc2.append(t[isignal][i])
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elif x[1].data[j-1] > 0 and x[1].data[j] <= 0:
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zc2.append(t[isignal][i])
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if x[2].data[j-1] <= 0 and x[2].data[j] >= 0:
|
|
||||||
zc3.append(t[isignal][i])
|
|
||||||
elif x[2].data[j-1] > 0 and x[2].data[j] <= 0:
|
|
||||||
zc3.append(t[isignal][i])
|
|
||||||
if len(zc1) >= 3 and len(zc2) >= 3 and len(zc3) >= 3:
|
|
||||||
break
|
|
||||||
Ts = max([max(np.diff(zc1)), max(np.diff(zc2)), max(np.diff(zc3))])
|
|
||||||
|
|
||||||
#Ts/4 is assumed as time difference between most likely and earliest possible pick!
|
|
||||||
self.EPick = ti - Ts/4
|
|
||||||
|
|
||||||
#get symmetric pick error as mean from earliest and latest possible pick
|
|
||||||
#by weighting latest possible pick tow times earliest possible pick
|
|
||||||
diffti_tl = self.LPick - ti
|
|
||||||
diffti_te = ti - self.EPick
|
|
||||||
self.PickError = (diffti_te + 2 * diffti_tl) / 3
|
|
||||||
|
|
||||||
if self.iplot is not None:
|
|
||||||
plt.figure(self.iplot)
|
|
||||||
if len(x) == 1:
|
|
||||||
p1, = plt.plot(t, x[0].data, 'k')
|
|
||||||
p2, = plt.plot(t[inoise], x[0].data[inoise])
|
|
||||||
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
|
|
||||||
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
|
||||||
p5, = plt.plot(zc, [0, 0, 0], '*g', markersize=14)
|
|
||||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
|
|
||||||
loc='best')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
|
||||||
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
|
|
||||||
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
|
||||||
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
|
||||||
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
|
||||||
plt.yticks([])
|
|
||||||
ax = plt.gca()
|
|
||||||
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
|
|
||||||
plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
|
|
||||||
elif len(x) == 2:
|
|
||||||
plt.subplot(2,1,1)
|
|
||||||
p1, = plt.plot(t, x[0].data, 'k')
|
|
||||||
p2, = plt.plot(t[inoise], x[0].data[inoise])
|
|
||||||
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
|
|
||||||
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
|
||||||
p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
|
|
||||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
|
|
||||||
loc='best')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
|
||||||
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
|
|
||||||
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
|
||||||
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
|
||||||
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot(zc1[0:3], [0, 0, 0], '*g')
|
|
||||||
plt.yticks([])
|
|
||||||
ax = plt.gca()
|
|
||||||
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
|
|
||||||
plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
|
|
||||||
plt.subplot(2,1,2)
|
|
||||||
plt.plot(t, x[1].data, 'k')
|
|
||||||
plt.plot(t[inoise], x[1].data[inoise])
|
|
||||||
plt.plot(t[isignal], x[1].data[isignal], 'r')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
|
||||||
plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
|
|
||||||
plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
|
||||||
plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
|
||||||
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
|
|
||||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
|
||||||
ax = plt.gca()
|
|
||||||
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
|
|
||||||
plt.yticks([])
|
|
||||||
elif len(x) == 3:
|
|
||||||
plt.subplot(3,1,1)
|
|
||||||
p1, = plt.plot(t, x[0].data, 'k')
|
|
||||||
p2, = plt.plot(t[inoise], x[0].data[inoise])
|
|
||||||
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
|
|
||||||
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
|
||||||
p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
|
|
||||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
|
|
||||||
loc='best')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
|
||||||
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
|
|
||||||
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
|
||||||
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
|
||||||
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.yticks([])
|
|
||||||
ax = plt.gca()
|
|
||||||
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
|
|
||||||
plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
|
|
||||||
plt.subplot(3,1,2)
|
|
||||||
plt.plot(t, x[1].data, 'k')
|
|
||||||
plt.plot(t[inoise], x[1].data[inoise])
|
|
||||||
plt.plot(t[isignal], x[1].data[isignal], 'r')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
|
||||||
plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
|
|
||||||
plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
|
||||||
plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
|
||||||
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
|
|
||||||
plt.yticks([])
|
|
||||||
ax = plt.gca()
|
|
||||||
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
|
|
||||||
plt.subplot(3,1,3)
|
|
||||||
plt.plot(t, x[2].data, 'k')
|
|
||||||
plt.plot(t[inoise], x[2].data[inoise])
|
|
||||||
plt.plot(t[isignal], x[2].data[isignal], 'r')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
|
||||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
|
||||||
plt.plot([ti, ti], [max(x[2].data), -max(x[2].data)], 'b', linewidth=2)
|
|
||||||
plt.plot([self.LPick, self.LPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
|
|
||||||
plt.plot([self.EPick, self.EPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
|
|
||||||
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
|
|
||||||
plt.plot(zc3[0:3], [0, 0, 0], '*g', markersize=14)
|
|
||||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
|
||||||
plt.yticks([])
|
|
||||||
ax = plt.gca()
|
|
||||||
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
|
|
||||||
plt.show()
|
|
||||||
raw_input()
|
|
||||||
plt.close(self.iplot)
|
|
||||||
|
|
||||||
elif self.getpick1() == None:
|
|
||||||
print 'EarlLatePicker: No initial onset time given! Check input!'
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user