clean-up to meet coding conventions
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@ -14,6 +14,8 @@ 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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import pdb
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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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@ -59,7 +61,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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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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print 'earllatepicker: Signal lower than noise level!'
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print 'earllatepicker: Signal lower than noise level!'
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print 'Skip this trace!'
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return LPick, EPick, PickError
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il = min(np.min(ilup) if ilup.size else float('inf'),
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@ -186,11 +188,11 @@ 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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if imax1 == 0:
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
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if imax1 == 0:
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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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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@ -228,11 +230,11 @@ 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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if imax2 == 0:
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
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if imax1 == 0:
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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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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@ -367,7 +369,7 @@ def getnoisewin(t, t1, tnoise, tgap):
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# get noise window
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inoise, = np.where((t <= max([t1 - tgap, 0])) \
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& (t >= max([t1 - tnoise - tgap, 0])))
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& (t >= max([t1 - tnoise - tgap, 0])))
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if np.size(inoise) < 1:
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print 'getnoisewin: Empty array inoise, check noise window!'
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@ -391,7 +393,7 @@ def getsignalwin(t, t1, tsignal):
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# get signal window
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isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
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& (t >= t1))
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& (t >= t1))
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if np.size(isignal) < 1:
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print 'getsignalwin: Empty array isignal, check signal window!'
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@ -432,7 +434,7 @@ def getResolutionWindow(snr):
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else:
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time_resolution = res_wins['HRW']
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return time_resolution/2
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return time_resolution / 2
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def wadaticheck(pickdic, dttolerance, iplot):
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@ -460,22 +462,21 @@ def wadaticheck(pickdic, dttolerance, iplot):
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SPtimes = []
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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'])
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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Spicks.append(UTCSpick.timestamp)
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SPtimes.append(spt)
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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'])
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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Spicks.append(UTCSpick.timestamp)
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SPtimes.append(spt)
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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 vp/vs ratio before check
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@ -499,48 +500,50 @@ 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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checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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checkedPpicks.append(checkedPpick.timestamp)
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checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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checkedSpicks.append(checkedSpick.timestamp)
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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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pickdic[key]['S']['marked'] = marker
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if len(checkedPpicks) >= 3:
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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 vp/vs ratio after check
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cvpvsr = p2[0] + 1
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print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
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# calculate vp/vs ratio after check
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cvpvsr = p2[0] + 1
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print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
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else:
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print 'wadatacheck: Not enough checked S-P times available!'
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print 'Skip Wadati check!'
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print 'wadatacheck: Not enough checked S-P times available!'
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print 'Skip Wadati check!'
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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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iplot=2
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iplot = 2
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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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plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
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'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
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plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', \
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'Reliable S-Picks', 'Wadati 2'], loc='best')
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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.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
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'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
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plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', \
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'Reliable S-Picks', 'Wadati 2'],
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loc='best')
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else:
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plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
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plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
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plt.ylabel('S-P Times [s]')
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plt.xlabel('P Times [s]')
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@ -600,12 +603,12 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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# calculate minimum adjusted signal level
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minsiglevel = max(e[inoise]) * nfac
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# minimum adjusted number of samples over minimum signal level
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minnum = len(isignal) * minpercent/100
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minnum = len(isignal) * minpercent / 100
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# get number of samples above minimum adjusted signal level
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numoverthr = len(np.where(e[isignal] >= minsiglevel)[0])
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if numoverthr >= minnum:
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print 'checksignallength: Signal reached required length.'
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print 'checksignallength: Signal reached required length.'
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returnflag = 1
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else:
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print 'checksignallength: Signal shorter than required minimum signal length!'
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@ -614,17 +617,18 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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if iplot == 2:
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plt.figure(iplot)
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p1, = plt.plot(t,x, 'k')
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p1, = plt.plot(t, x, 'k')
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p2, = plt.plot(t[inoise], e[inoise], 'c')
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p3, = plt.plot(t[isignal],e[isignal], 'r')
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p3, = plt.plot(t[isignal], e[isignal], 'r')
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p2, = plt.plot(t[inoise], e[inoise])
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p3, = plt.plot(t[isignal],e[isignal], 'r')
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p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \
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[minsiglevel, minsiglevel], 'g')
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p3, = plt.plot(t[isignal], e[isignal], 'r')
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p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]], \
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[minsiglevel, minsiglevel], 'g')
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p5, = plt.plot([pick, pick], [min(x), max(x)], 'b', linewidth=2)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \
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'Envelope Signal Window', 'Minimum Signal Level', \
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'Onset'], loc='best')
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'Envelope Signal Window',
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'Minimum Signal Level', \
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'Onset'], loc='best')
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plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
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plt.ylabel('Counts')
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plt.title('Check for Signal Length, Station %s' % X[0].stats.station)
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@ -660,14 +664,14 @@ def checkPonsets(pickdic, dttolerance, iplot):
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stations = []
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for key in pickdic:
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if pickdic[key]['P']['weight'] < 4:
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# add P onsets to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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stations.append(key)
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# add P onsets to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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stations.append(key)
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# apply jackknife bootstrapping on variance of P onsets
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print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...'
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[xjack,PHI_pseudo,PHI_sub] = jackknife(Ppicks, 'VAR', 1)
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[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
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# get pseudo variances smaller than average variances
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# these picks passed jackknife test
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ij = np.where(PHI_pseudo <= xjack)
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@ -686,46 +690,48 @@ def checkPonsets(pickdic, dttolerance, iplot):
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badstations = np.array(stations)[ibad]
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print 'checkPonset: Skipped %d P onsets out of %d' % (len(badstations) \
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+ len(badjkstations), len(stations))
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+ len(badjkstations),
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len(stations))
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goodmarker = 'goodPonsetcheck'
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badmarker = 'badPonsetcheck'
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badjkmarker = 'badjkcheck'
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for i in range(0, len(goodstations)):
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# mark P onset as checked and keep P weight
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pickdic[goodstations[i]]['P']['marked'] = goodmarker
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pickdic[goodstations[i]]['P']['marked'] = goodmarker
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for i in range(0, len(badstations)):
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# mark P onset and downgrade P weight to 9
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# (not used anymore)
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pickdic[badstations[i]]['P']['marked'] = badmarker
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pickdic[badstations[i]]['P']['weight'] = 9
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# mark P onset and downgrade P weight to 9
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# (not used anymore)
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pickdic[badstations[i]]['P']['marked'] = badmarker
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pickdic[badstations[i]]['P']['weight'] = 9
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for i in range(0, len(badjkstations)):
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# mark P onset and downgrade P weight to 9
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# (not used anymore)
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pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
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pickdic[badjkstations[i]]['P']['weight'] = 9
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# mark P onset and downgrade P weight to 9
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# (not used anymore)
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pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
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pickdic[badjkstations[i]]['P']['weight'] = 9
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checkedonsets = pickdic
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iplot = 2
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if iplot > 1:
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p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
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p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
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p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14)
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p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', \
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linewidth=2)
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linewidth=2)
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for i in range(0, len(Ppicks)):
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plt.text(i, Ppicks[i] + 0.2, stations[i])
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plt.text(i, Ppicks[i] + 0.2, stations[i])
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plt.xlabel('Number of P Picks')
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plt.ylabel('Onset Time [s] from 1.1.1970')
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plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'], \
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loc='best')
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loc='best')
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plt.title('Check P Onsets')
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plt.show()
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raw_input()
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return checkedonsets
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def jackknife(X, phi, h):
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'''
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Function to calculate the Jackknife Estimator for a given quantity,
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@ -753,44 +759,44 @@ def jackknife(X, phi, h):
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g = len(X) / h
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if type(g) is not int:
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print 'jackknife: Cannot divide quantity X in equal sized subgroups!'
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print 'jackknife: Cannot divide quantity X in equal sized subgroups!'
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print 'Choose another size for subgroups!'
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return PHI_jack, PHI_pseudo, PHI_sub
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else:
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# estimator of undisturbed spot check
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if phi == 'MEA':
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phi_sc = np.mean(X)
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# estimator of undisturbed spot check
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if phi == 'MEA':
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phi_sc = np.mean(X)
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elif phi == 'VAR':
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phi_sc = np.var(X)
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phi_sc = np.var(X)
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elif phi == 'MED':
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phi_sc = np.median(X)
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phi_sc = np.median(X)
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# estimators of subgroups
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# estimators of subgroups
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PHI_pseudo = []
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PHI_sub = []
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for i in range(0, g - 1):
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# subgroup i, remove i-th sample
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xx = X[:]
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del xx[i]
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# calculate estimators of disturbed spot check
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if phi == 'MEA':
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phi_sub = np.mean(xx)
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elif phi == 'VAR':
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phi_sub = np.var(xx)
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elif phi == 'MED':
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phi_sub = np.median(xx)
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# subgroup i, remove i-th sample
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xx = X[:]
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del xx[i]
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# calculate estimators of disturbed spot check
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if phi == 'MEA':
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phi_sub = np.mean(xx)
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elif phi == 'VAR':
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phi_sub = np.var(xx)
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elif phi == 'MED':
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phi_sub = np.median(xx)
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PHI_sub.append(phi_sub)
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# pseudo values
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phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
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PHI_pseudo.append(phi_pseudo)
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PHI_sub.append(phi_sub)
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# pseudo values
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phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
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PHI_pseudo.append(phi_pseudo)
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# jackknife estimator
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PHI_jack = np.mean(PHI_pseudo)
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return PHI_jack, PHI_pseudo, PHI_sub
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if __name__ == '__main__':
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import doctest
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doctest.testmod()
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