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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import warnings
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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'''
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Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
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@ -48,13 +49,13 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
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X[0].stats.delta)
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# get latest possible pick
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#get noise window
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# get noise window
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inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
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#get signal window
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# get signal window
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isignal = getsignalwin(t, Pick1, TSNR[2])
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#calculate noise level
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# calculate noise level
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nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
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#get time where signal exceeds nlevel
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# get time where signal exceeds nlevel
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ilup, = np.where(x[isignal] > nlevel)
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ildown, = np.where(x[isignal] < -nlevel)
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if not ilup.size and not ildown.size:
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@ -63,17 +64,17 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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np.min(ildown) if ildown.size else float('inf'))
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LPick = t[isignal][il]
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#get earliest possible pick
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# get earliest possible pick
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#determine all zero crossings in signal window (demeaned)
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# determine all zero crossings in signal window (demeaned)
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zc = crossings_nonzero_all(x[isignal] - x[isignal].mean())
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#calculate mean half period T0 of signal as the average of the
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T0 = np.mean(np.diff(zc)) * X[0].stats.delta #this is half wave length!
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#T0/4 is assumed as time difference between most likely and earliest possible pick!
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# calculate mean half period T0 of signal as the average of the
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T0 = np.mean(np.diff(zc)) * X[0].stats.delta # this is half wave length!
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# T0/4 is assumed as time difference between most likely and earliest possible pick!
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EPick = Pick1 - T0 / 2
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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 two times earliest possible pick
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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 two times earliest possible pick
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diffti_tl = LPick - Pick1
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diffti_te = Pick1 - EPick
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PickError = (diffti_te + 2 * diffti_tl) / 3
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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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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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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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['Data', 'Noise Window', 'Signal Window', 'Noise Level',
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'Zero Crossings'], \
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@ -149,13 +151,13 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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# get pick window
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ipick = np.where(
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(t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
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#remove mean
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# remove mean
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xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
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xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
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#get next zero crossing after most likely pick
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#initial onset is assumed to be the first zero crossing
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#first from unfiltered trace
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# get next zero crossing after most likely pick
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# initial onset is assumed to be the first zero crossing
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# first from unfiltered trace
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zc1 = []
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zc1.append(Pick)
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index1 = []
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@ -171,9 +173,9 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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if len(zc1) == 3:
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break
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#if time difference betweeen 1st and 2cnd zero crossing
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#is too short, get time difference between 1st and 3rd
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#to derive maximum
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# if time difference betweeen 1st and 2cnd zero crossing
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# is too short, get time difference between 1st and 3rd
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# to derive maximum
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if zc1[1] - zc1[0] <= Xraw[0].stats.delta:
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li1 = index1[1]
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else:
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@ -184,13 +186,13 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
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islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
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#calculate slope as polynomal fit of order 1
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# calculate slope as polynomal fit of order 1
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xslope1 = np.arange(0, len(xraw[islope1]), 1)
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P1 = np.polyfit(xslope1, xraw[islope1], 1)
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datafit1 = np.polyval(P1, xslope1)
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#now using filterd trace
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#next zero crossing after most likely pick
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# now using filterd trace
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# next zero crossing after most likely pick
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zc2 = []
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zc2.append(Pick)
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index2 = []
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@ -206,9 +208,9 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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if len(zc2) == 3:
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break
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#if time difference betweeen 1st and 2cnd zero crossing
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#is too short, get time difference between 1st and 3rd
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#to derive maximum
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# if time difference betweeen 1st and 2cnd zero crossing
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# is too short, get time difference between 1st and 3rd
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# to derive maximum
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if zc2[1] - zc2[0] <= Xfilt[0].stats.delta:
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li2 = index2[1]
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else:
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@ -219,12 +221,12 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
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islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
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#calculate slope as polynomal fit of order 1
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# calculate slope as polynomal fit of order 1
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xslope2 = np.arange(0, len(xfilt[islope2]), 1)
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P2 = np.polyfit(xslope2, xfilt[islope2], 1)
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datafit2 = np.polyval(P2, xslope2)
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#compare results
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# compare results
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if P1 is not None and P2 is not None:
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if P1[0] < 0 and P2[0] < 0:
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FM = 'D'
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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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def crossings_nonzero_all(data):
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pos = data > 0
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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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def getSNR(X, TSNR, t1):
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'''
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Function to calculate SNR of certain part of seismogram relative to
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@ -311,7 +315,7 @@ def getSNR(X, TSNR, t1):
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# get noise window
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inoise = getnoisewin(t, t1, TSNR[0], TSNR[1])
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#get signal window
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# get signal window
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isignal = getsignalwin(t, t1, TSNR[2])
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if np.size(inoise) < 1:
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print 'getSNR: Empty array inoise, check noise window!'
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@ -320,7 +324,7 @@ def getSNR(X, TSNR, t1):
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print 'getSNR: Empty array isignal, check signal window!'
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return
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#calculate ratios
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# calculate ratios
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noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
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signallevel = np.sqrt(np.mean(np.square(x[isignal])))
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SNR = signallevel / noiselevel
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@ -382,6 +386,7 @@ def getsignalwin(t, t1, tsignal):
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return isignal
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def wadaticheck(pickdic, dttolerance, iplot):
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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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@ -408,23 +413,28 @@ def wadaticheck(pickdic, dttolerance, iplot):
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vpvs = []
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for key in pickdic:
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if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
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# calculate S-P time
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spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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# add S-P time to dictionary
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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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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
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Ppicks.append(UTCPpick)
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Spicks.append(UTCSpick)
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SPtimes.append(spt)
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vpvs.append(UTCPpick/UTCSpick)
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# calculate S-P time
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spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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# add S-P time to dictionary
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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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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,
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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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Spicks.append(UTCSpick)
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SPtimes.append(spt)
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vpvs.append(UTCPpick / UTCSpick)
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if len(SPtimes) >= 3:
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# calculate slope
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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wdfit = np.polyval(p1, Ppicks)
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# calculate slope
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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wdfit = np.polyval(p1, Ppicks)
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wfitflag = 0
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# calculate average vp/vs ratio before check
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@ -439,7 +449,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
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for key in pickdic:
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if pickdic[key].has_key('SPt'):
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ii = 0
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wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
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wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
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ii += 1
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# check, if deviation is larger than adjusted
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if wddiff >= dttolerance:
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@ -449,22 +459,23 @@ def wadaticheck(pickdic, dttolerance, iplot):
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pickdic[key]['S']['weight'] = 9
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else:
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marker = 'goodWadatiCheck'
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checkedPpick = UTCDateTime(pickdic[key]['P']['mpp']) - \
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UTCDateTime(1970,1,1,0,0,0)
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checkedPpick = UTCDateTime(pickdic[key]['P']['mpp']) - \
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UTCDateTime(1970, 1, 1, 0, 0, 0)
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checkedPpicks.append(checkedPpick)
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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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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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checkedvpvs.append(checkedPpick/checkedSpick)
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checkedvpvs.append(checkedPpick / checkedSpick)
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pickdic[key]['S']['marked'] = marker
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# calculate new slope
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p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
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wdfit2 = np.polyval(p2, checkedPpicks)
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# calculate new slope
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p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
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wdfit2 = np.polyval(p2, checkedPpicks)
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# calculate average vp/vs ratio after check
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cvpvsr = p2[0] + 1
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@ -473,24 +484,26 @@ def wadaticheck(pickdic, dttolerance, iplot):
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checkedonsets = pickdic
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else:
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print 'wadaticheck: Not enough S-P times available for reliable regression!'
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print 'wadaticheck: Not enough S-P times available for reliable regression!'
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print 'Skip wadati check!'
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wfitflag = 1
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# plot results
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if iplot > 1:
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plt.figure(iplot)
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f1, = plt.plot(Ppicks, SPtimes, 'ro')
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plt.figure(iplot)
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f1, = plt.plot(Ppicks, SPtimes, 'ro')
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if wfitflag == 0:
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f2, = plt.plot(Ppicks, wdfit, 'k')
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f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
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f4, = plt.plot(checkedPpicks, wdfit2, 'g')
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f2, = plt.plot(Ppicks, wdfit, 'k')
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f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
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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.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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% (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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'Wadati 2'], loc='best')
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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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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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plt.show()
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raw_input()
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plt.close(iplot)
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