Cosmetics, changed print commands to keep compatibility to Python 3.
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Ludger Küperkoch
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60b9f176f0
@ -7,7 +7,6 @@
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:author: Ludger Kueperkoch / MAGS2 EP3 working group
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"""
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import numpy as np
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import scipy as sc
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import matplotlib.pyplot as plt
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@ -44,7 +43,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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LPick = None
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EPick = None
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PickError = None
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print 'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
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print ("earllatepicker: Get earliest and latest possible pick relative to most likely pick ...")
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x = X[0].data
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t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
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@ -60,8 +59,8 @@ 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 'Skip this trace!'
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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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np.min(ildown) if ildown.size else float('inf'))
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@ -143,7 +142,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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FM = None
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if Pick is not None:
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print 'fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...'
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print ("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
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xraw = Xraw[0].data
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xfilt = Xfilt[0].data
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@ -182,15 +181,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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li1 = index1[0]
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if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
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print 'fmpicker: Onset on unfiltered trace too emergent for first motion determination!'
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print ("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
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P1 = 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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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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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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@ -224,15 +223,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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li2 = index2[0]
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if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
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print 'fmpicker: Onset on filtered trace too emergent for first motion determination!'
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print ("fmpicker: Onset on filtered trace too emergent for first motion determination!")
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P2 = 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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if imax2 == 0:
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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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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@ -256,7 +255,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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elif P1[0] > 0 and P2[0] <= 0:
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FM = '+'
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print 'fmpicker: Found polarity %s' % FM
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print ("fmpicker: Found polarity %s" % FM)
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if iplot > 1:
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plt.figure(iplot)
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@ -331,10 +330,10 @@ def getSNR(X, TSNR, t1):
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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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print ("getSNR: Empty array inoise, check noise window!")
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return
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elif np.size(isignal) < 1:
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print 'getSNR: Empty array isignal, check signal window!'
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print ("getSNR: Empty array isignal, check signal window!")
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return
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# demean over entire waveform
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@ -372,7 +371,7 @@ def getnoisewin(t, t1, tnoise, tgap):
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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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if np.size(inoise) < 1:
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print 'getnoisewin: Empty array inoise, check noise window!'
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print ("getnoisewin: Empty array inoise, check noise window!")
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return inoise
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@ -396,7 +395,7 @@ def getsignalwin(t, t1, tsignal):
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isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
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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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print ("getsignalwin: Empty array isignal, check signal window!")
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return isignal
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@ -483,8 +482,8 @@ def wadaticheck(pickdic, dttolerance, iplot):
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# calculate vp/vs ratio before check
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vpvsr = p1[0] + 1
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print '###############################################'
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print 'wadaticheck: Average Vp/Vs ratio before check:', vpvsr
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print ("###############################################")
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print ("wadaticheck: Average Vp/Vs ratio before check:", vpvsr)
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checkedPpicks = []
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checkedSpicks = []
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@ -521,18 +520,18 @@ def wadaticheck(pickdic, dttolerance, iplot):
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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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print 'wadatacheck: Skipped %d S pick(s).' % ibad
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print ("wadaticheck: Average Vp/Vs ratio after check:", cvpvsr)
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print ("wadatacheck: Skipped %d S pick(s)." % ibad)
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else:
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print '###############################################'
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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 ("###############################################")
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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 'Skip wadati check!'
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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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@ -592,7 +591,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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print 'Checking signal length ...'
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print ("Checking signal length ...")
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x = X[0].data
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t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
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@ -601,8 +600,8 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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# generate envelope function from Hilbert transform
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y = np.imag(sc.signal.hilbert(x))
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e = np.sqrt(np.power(x, 2) + np.power(y, 2))
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# get noise window
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inoise = getnoisewin(t, pick, TSNR[0], TSNR[1])
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# get noise window in front of pick plus saftey gap
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inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1])
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# get signal window
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isignal = getsignalwin(t, pick, TSNR[2])
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# calculate minimum adjusted signal level
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@ -613,12 +612,12 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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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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print 'Presumably picked noise peak, pick is rejected!'
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print '(min. signal length required:', minsiglength, 's)'
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print ("checksignallength: Signal shorter than required minimum signal length!")
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print ("Presumably picked noise peak, pick is rejected!")
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print ("(min. signal length required:', minsiglength, 's)'")
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returnflag = 0
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if iplot == 2:
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@ -629,7 +628,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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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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[minsiglevel, minsiglevel], 'g', linewidth=2)
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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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@ -675,8 +674,8 @@ def checkPonsets(pickdic, dttolerance, iplot):
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stations.append(key)
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# apply jackknife bootstrapping on variance of P onsets
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print '###############################################'
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print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...'
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print ("###############################################")
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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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# get pseudo variances smaller than average variances
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# (times safety factor), these picks passed jackknife test
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@ -684,7 +683,7 @@ def checkPonsets(pickdic, dttolerance, iplot):
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# these picks did not pass jackknife test
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badjk = np.where(PHI_pseudo > 2 * xjack)
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badjkstations = np.array(stations)[badjk]
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print 'checkPonsets: %d pick(s) did not pass jackknife test!' % len(badjkstations)
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print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
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# calculate median from these picks
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pmedian = np.median(np.array(Ppicks)[ij])
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@ -696,9 +695,9 @@ def checkPonsets(pickdic, dttolerance, iplot):
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goodstations = np.array(stations)[igood]
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badstations = np.array(stations)[ibad]
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print 'checkPonsets: %d pick(s) deviate too much from median!' % len(ibad)
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print 'checkPonsets: Skipped %d P pick(s) out of %d' % (len(badstations) \
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+ len(badjkstations), len(stations))
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print ("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
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print ("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
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+ len(badjkstations), len(stations)))
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goodmarker = 'goodPonsetcheck'
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badmarker = 'badPonsetcheck'
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@ -765,8 +764,8 @@ 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 'Choose another size for 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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@ -834,7 +833,7 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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print 'Check for spuriously picked S onset instead of P onset ...'
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print ("Check for spuriously picked S onset instead of P onset ...")
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returnflag = 0
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@ -875,9 +874,9 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
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# vertical P-coda level must exceed horizontal P-coda level
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# zfac times encodalevel
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if zcodalevel < minsiglevel:
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print 'checkZ4S: Maybe S onset? Skip this P pick!'
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print ("checkZ4S: Maybe S onset? Skip this P pick!")
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else:
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print 'checkZ4S: P onset passes checkZ4S test!'
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print ("checkZ4S: P onset passes checkZ4S test!")
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returnflag = 1
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if iplot > 1:
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