reformatting code to avoid indentation inconsistencies
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@ -13,6 +13,7 @@ import matplotlib.pyplot as plt
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from obspy.core import Stream, UTCDateTime
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from obspy.core import Stream, UTCDateTime
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import warnings
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import warnings
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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'''
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'''
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Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
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Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
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@ -84,7 +85,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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p2, = plt.plot(t[inoise], x[inoise])
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p2, = plt.plot(t[inoise], x[inoise])
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p3, = plt.plot(t[isignal], x[isignal], 'r')
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p3, = plt.plot(t[isignal], x[isignal], 'r')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p5, = plt.plot(t[isignal[0][zc]], np.zeros(len(zc)), '*g', markersize=14)
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p5, = plt.plot(t[isignal[0][zc]], np.zeros(len(zc)), '*g',
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markersize=14)
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plt.legend([p1, p2, p3, p4, p5],
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plt.legend([p1, p2, p3, p4, p5],
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['Data', 'Noise Window', 'Signal Window', 'Noise Level',
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['Data', 'Noise Window', 'Signal Window', 'Noise Level',
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'Zero Crossings'], \
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'Zero Crossings'], \
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@ -280,11 +282,13 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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return FM
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return FM
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def crossings_nonzero_all(data):
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def crossings_nonzero_all(data):
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pos = data > 0
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pos = data > 0
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npos = ~pos
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npos = ~pos
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return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
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return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
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def getSNR(X, TSNR, t1):
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def getSNR(X, TSNR, t1):
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'''
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'''
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Function to calculate SNR of certain part of seismogram relative to
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Function to calculate SNR of certain part of seismogram relative to
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@ -382,6 +386,7 @@ def getsignalwin(t, t1, tsignal):
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return isignal
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return isignal
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def wadaticheck(pickdic, dttolerance, iplot):
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def wadaticheck(pickdic, dttolerance, iplot):
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'''
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'''
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Function to calculate Wadati-diagram from given P and S onsets in order
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Function to calculate Wadati-diagram from given P and S onsets in order
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@ -413,14 +418,19 @@ def wadaticheck(pickdic, dttolerance, iplot):
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# add S-P time to dictionary
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# add S-P time to dictionary
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pickdic[key]['SPt'] = spt
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pickdic[key]['SPt'] = spt
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# add P onsets and corresponding S-P times to list
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# add P onsets and corresponding S-P times to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
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1, 1,
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0, 0,
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0)
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(1970,
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1, 1,
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0, 0,
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0)
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Ppicks.append(UTCPpick)
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Ppicks.append(UTCPpick)
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Spicks.append(UTCSpick)
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Spicks.append(UTCSpick)
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SPtimes.append(spt)
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SPtimes.append(spt)
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vpvs.append(UTCPpick / UTCSpick)
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vpvs.append(UTCPpick / UTCSpick)
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if len(SPtimes) >= 3:
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if len(SPtimes) >= 3:
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# calculate slope
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# calculate slope
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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@ -455,7 +465,8 @@ def wadaticheck(pickdic, dttolerance, iplot):
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checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) - \
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checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) - \
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UTCDateTime(1970, 1, 1, 0, 0, 0)
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UTCDateTime(1970, 1, 1, 0, 0, 0)
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checkedSpicks.append(checkedSpick)
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checkedSpicks.append(checkedSpick)
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checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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checkedSPtime = pickdic[key]['S']['mpp'] - \
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pickdic[key]['P']['mpp']
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checkedSPtimes.append(checkedSPtime)
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checkedSPtimes.append(checkedSPtime)
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checkedvpvs.append(checkedPpick / checkedSpick)
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checkedvpvs.append(checkedPpick / checkedSpick)
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@ -487,9 +498,11 @@ def wadaticheck(pickdic, dttolerance, iplot):
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f4, = plt.plot(checkedPpicks, wdfit2, 'g')
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f4, = plt.plot(checkedPpicks, wdfit2, 'g')
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plt.ylabel('S-P Times [s]')
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plt.ylabel('S-P Times [s]')
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plt.xlabel('P Times [s]')
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plt.xlabel('P Times [s]')
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plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(old)=%5.2f, Vp/Vs(checked)=%5.2f' \
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plt.title(
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'Wadati-Diagram, %d S-P Times, Vp/Vs(old)=%5.2f, Vp/Vs(checked)=%5.2f' \
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% (len(SPtimes), vpvsr, cvpvsr))
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% (len(SPtimes), vpvsr, cvpvsr))
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plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', 'Reliable S-Picks', \
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plt.legend([f1, f2, f3, f4],
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['Skipped S-Picks', 'Wadati 1', 'Reliable S-Picks', \
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'Wadati 2'], loc='best')
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'Wadati 2'], loc='best')
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plt.show()
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plt.show()
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raw_input()
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raw_input()
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