New attribute getPickError in class EarlLatePicker to derive symmetric picking error out of earliest and latest possible and most probable pick.
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Ludger Küperkoch
parent
a3fb4770c6
commit
380cccdf16
@ -133,6 +133,9 @@ class AutoPicking(object):
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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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return self.iplot
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@ -151,15 +154,18 @@ class AutoPicking(object):
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class AICPicker(AutoPicking):
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'''
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Method to derive onset time of arriving phase based on CF
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derived from AIC.
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Method to derive the onset time of an arriving phase based on CF
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derived from AIC. In order to get an impression of the quality of this inital pick,
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a quality assessment is applied based on SNR and slope determination derived from the CF,
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from which the AIC has been calculated.
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'''
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def calcPick(self):
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print 'AICPicker: Get 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.PickError = None
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#find NaN's
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nn = np.isnan(self.cf)
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if len(nn) > 1:
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@ -279,6 +285,7 @@ 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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self.Pick = 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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#smooth CF
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@ -377,6 +384,7 @@ class EarlLatePicker(AutoPicking):
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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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@ -509,6 +517,12 @@ class EarlLatePicker(AutoPicking):
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#Ts/4 is assumed as time difference between most likely and earliest possible pick!
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self.EPick = ti - Ts/4
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#get symmetric pick error as mean from earliest and latest possible pick
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#by weighting latest possible pick tow times earliest possible pick
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diffti_tl = self.LPick - ti
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diffti_te = ti - self.EPick
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self.PickError = (diffti_te + 2 * diffti_tl) / 3
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if self.iplot is not None:
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plt.figure(self.iplot)
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if len(x) == 1:
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@ -517,14 +531,19 @@ class EarlLatePicker(AutoPicking):
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p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p5, = plt.plot(zc, [0, 0, 0], '*g', markersize=14)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'])
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
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loc='best')
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plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
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plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
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plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
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plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
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plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.yticks([])
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plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station)
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ax = plt.gca()
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ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
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plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
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elif len(x) == 2:
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plt.subplot(2,1,1)
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p1, = plt.plot(t, x[0].data, 'k')
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@ -532,14 +551,19 @@ class EarlLatePicker(AutoPicking):
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p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'])
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
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loc='best')
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plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
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plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
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plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
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plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
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plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot(zc1[0:3], [0, 0, 0], '*g')
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plt.yticks([])
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plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station)
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ax = plt.gca()
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ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
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plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
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plt.subplot(2,1,2)
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plt.plot(t, x[1].data, 'k')
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plt.plot(t[inoise], x[1].data[inoise])
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@ -549,8 +573,12 @@ class EarlLatePicker(AutoPicking):
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plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
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plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
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plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
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plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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ax = plt.gca()
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ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
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plt.yticks([])
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elif len(x) == 3:
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plt.subplot(3,1,1)
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@ -559,13 +587,18 @@ class EarlLatePicker(AutoPicking):
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p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'])
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
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loc='best')
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plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
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plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
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plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
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plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
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plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.yticks([])
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plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station)
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ax = plt.gca()
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ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
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plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
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plt.subplot(3,1,2)
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plt.plot(t, x[1].data, 'k')
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plt.plot(t[inoise], x[1].data[inoise])
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@ -575,8 +608,12 @@ class EarlLatePicker(AutoPicking):
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plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
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plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
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plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
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plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
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plt.yticks([])
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ax = plt.gca()
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ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
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plt.subplot(3,1,3)
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plt.plot(t, x[2].data, 'k')
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plt.plot(t[inoise], x[2].data[inoise])
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@ -586,9 +623,13 @@ class EarlLatePicker(AutoPicking):
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plt.plot([ti, ti], [max(x[2].data), -max(x[2].data)], 'b', linewidth=2)
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plt.plot([self.LPick, self.LPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
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plt.plot([self.EPick, self.EPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
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plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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plt.plot(zc3[0:3], [0, 0, 0], '*g', markersize=14)
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.yticks([])
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ax = plt.gca()
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ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
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plt.show()
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raw_input()
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plt.close(self.iplot)
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