From 60b9f176f0608f5db1a2c43dff2aa77200a352a1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ludger=20K=C3=BCperkoch?= Date: Thu, 3 Sep 2015 14:55:25 +0200 Subject: [PATCH] Cosmetics, changed print commands to keep compatibility to Python 3. --- pylot/core/pick/utils.py | 87 ++++++++++++++++++++-------------------- 1 file changed, 43 insertions(+), 44 deletions(-) diff --git a/pylot/core/pick/utils.py b/pylot/core/pick/utils.py index 161942cb..f891b6fe 100644 --- a/pylot/core/pick/utils.py +++ b/pylot/core/pick/utils.py @@ -7,7 +7,6 @@ :author: Ludger Kueperkoch / MAGS2 EP3 working group """ - import numpy as np import scipy as sc import matplotlib.pyplot as plt @@ -44,7 +43,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None): LPick = None EPick = None PickError = None - print 'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...' + print ("earllatepicker: Get earliest and latest possible pick relative to most likely pick ...") x = X[0].data t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate, @@ -60,8 +59,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None): ilup, = np.where(x[isignal] > nlevel) ildown, = np.where(x[isignal] < -nlevel) if not ilup.size and not ildown.size: - print 'earllatepicker: Signal lower than noise level!' - print 'Skip this trace!' + print ("earllatepicker: Signal lower than noise level!") + print ("Skip this trace!") return LPick, EPick, PickError il = min(np.min(ilup) if ilup.size else float('inf'), np.min(ildown) if ildown.size else float('inf')) @@ -143,7 +142,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None): FM = None if Pick is not None: - print 'fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...' + print ("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...") xraw = Xraw[0].data xfilt = Xfilt[0].data @@ -182,15 +181,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None): else: li1 = index1[0] if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0: - print 'fmpicker: Onset on unfiltered trace too emergent for first motion determination!' + print ("fmpicker: Onset on unfiltered trace too emergent for first motion determination!") P1 = None else: imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]])) if imax1 == 0: imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]])) if imax1 == 0: - print 'fmpicker: Zero crossings too close!' - print 'Skip first motion determination!' + print ("fmpicker: Zero crossings too close!") + print ("Skip first motion determination!") return FM islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1])) @@ -224,15 +223,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None): else: li2 = index2[0] if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0: - print 'fmpicker: Onset on filtered trace too emergent for first motion determination!' + print ("fmpicker: Onset on filtered trace too emergent for first motion determination!") P2 = None else: imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]])) if imax2 == 0: imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]])) if imax2 == 0: - print 'fmpicker: Zero crossings too close!' - print 'Skip first motion determination!' + print ("fmpicker: Zero crossings too close!") + print ("Skip first motion determination!") return FM islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2])) @@ -256,7 +255,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None): elif P1[0] > 0 and P2[0] <= 0: FM = '+' - print 'fmpicker: Found polarity %s' % FM + print ("fmpicker: Found polarity %s" % FM) if iplot > 1: plt.figure(iplot) @@ -331,10 +330,10 @@ def getSNR(X, TSNR, t1): # get signal window isignal = getsignalwin(t, t1, TSNR[2]) if np.size(inoise) < 1: - print 'getSNR: Empty array inoise, check noise window!' + print ("getSNR: Empty array inoise, check noise window!") return elif np.size(isignal) < 1: - print 'getSNR: Empty array isignal, check signal window!' + print ("getSNR: Empty array isignal, check signal window!") return # demean over entire waveform @@ -372,7 +371,7 @@ def getnoisewin(t, t1, tnoise, tgap): inoise, = np.where((t <= max([t1 - tgap, 0])) \ & (t >= max([t1 - tnoise - tgap, 0]))) if np.size(inoise) < 1: - print 'getnoisewin: Empty array inoise, check noise window!' + print ("getnoisewin: Empty array inoise, check noise window!") return inoise @@ -396,7 +395,7 @@ def getsignalwin(t, t1, tsignal): isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \ & (t >= t1)) if np.size(isignal) < 1: - print 'getsignalwin: Empty array isignal, check signal window!' + print ("getsignalwin: Empty array isignal, check signal window!") return isignal @@ -483,8 +482,8 @@ def wadaticheck(pickdic, dttolerance, iplot): # calculate vp/vs ratio before check vpvsr = p1[0] + 1 - print '###############################################' - print 'wadaticheck: Average Vp/Vs ratio before check:', vpvsr + print ("###############################################") + print ("wadaticheck: Average Vp/Vs ratio before check:", vpvsr) checkedPpicks = [] checkedSpicks = [] @@ -521,18 +520,18 @@ def wadaticheck(pickdic, dttolerance, iplot): # calculate vp/vs ratio after check cvpvsr = p2[0] + 1 - print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr - print 'wadatacheck: Skipped %d S pick(s).' % ibad + print ("wadaticheck: Average Vp/Vs ratio after check:", cvpvsr) + print ("wadatacheck: Skipped %d S pick(s)." % ibad) else: - print '###############################################' - print 'wadatacheck: Not enough checked S-P times available!' - print 'Skip Wadati check!' + print ("###############################################") + print ("wadatacheck: Not enough checked S-P times available!") + print ("Skip Wadati check!") checkedonsets = pickdic else: - print 'wadaticheck: Not enough S-P times available for reliable regression!' - print 'Skip wadati check!' + print ("wadaticheck: Not enough S-P times available for reliable regression!") + print ("Skip wadati check!") wfitflag = 1 # plot results @@ -592,7 +591,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot): assert isinstance(X, Stream), "%s is not a stream object" % str(X) - print 'Checking signal length ...' + print ("Checking signal length ...") x = X[0].data t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate, @@ -601,8 +600,8 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot): # generate envelope function from Hilbert transform y = np.imag(sc.signal.hilbert(x)) e = np.sqrt(np.power(x, 2) + np.power(y, 2)) - # get noise window - inoise = getnoisewin(t, pick, TSNR[0], TSNR[1]) + # get noise window in front of pick plus saftey gap + inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1]) # get signal window isignal = getsignalwin(t, pick, TSNR[2]) # calculate minimum adjusted signal level @@ -613,12 +612,12 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot): numoverthr = len(np.where(e[isignal] >= minsiglevel)[0]) if numoverthr >= minnum: - print 'checksignallength: Signal reached required length.' + print ("checksignallength: Signal reached required length.") returnflag = 1 else: - print 'checksignallength: Signal shorter than required minimum signal length!' - print 'Presumably picked noise peak, pick is rejected!' - print '(min. signal length required:', minsiglength, 's)' + print ("checksignallength: Signal shorter than required minimum signal length!") + print ("Presumably picked noise peak, pick is rejected!") + print ("(min. signal length required:', minsiglength, 's)'") returnflag = 0 if iplot == 2: @@ -629,7 +628,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot): p2, = plt.plot(t[inoise], e[inoise]) p3, = plt.plot(t[isignal],e[isignal], 'r') p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \ - [minsiglevel, minsiglevel], 'g') + [minsiglevel, minsiglevel], 'g', linewidth=2) p5, = plt.plot([pick, pick], [min(x), max(x)], 'b', linewidth=2) plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \ 'Envelope Signal Window', 'Minimum Signal Level', \ @@ -675,8 +674,8 @@ def checkPonsets(pickdic, dttolerance, iplot): stations.append(key) # apply jackknife bootstrapping on variance of P onsets - print '###############################################' - print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...' + print ("###############################################") + print ("checkPonsets: Apply jackknife bootstrapping on P-onset times ...") [xjack,PHI_pseudo,PHI_sub] = jackknife(Ppicks, 'VAR', 1) # get pseudo variances smaller than average variances # (times safety factor), these picks passed jackknife test @@ -684,7 +683,7 @@ def checkPonsets(pickdic, dttolerance, iplot): # these picks did not pass jackknife test badjk = np.where(PHI_pseudo > 2 * xjack) badjkstations = np.array(stations)[badjk] - print 'checkPonsets: %d pick(s) did not pass jackknife test!' % len(badjkstations) + print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations)) # calculate median from these picks pmedian = np.median(np.array(Ppicks)[ij]) @@ -696,9 +695,9 @@ def checkPonsets(pickdic, dttolerance, iplot): goodstations = np.array(stations)[igood] badstations = np.array(stations)[ibad] - print 'checkPonsets: %d pick(s) deviate too much from median!' % len(ibad) - print 'checkPonsets: Skipped %d P pick(s) out of %d' % (len(badstations) \ - + len(badjkstations), len(stations)) + print ("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad)) + print ("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \ + + len(badjkstations), len(stations))) goodmarker = 'goodPonsetcheck' badmarker = 'badPonsetcheck' @@ -765,8 +764,8 @@ def jackknife(X, phi, h): g = len(X) / h if type(g) is not int: - print 'jackknife: Cannot divide quantity X in equal sized subgroups!' - print 'Choose another size for subgroups!' + print ("jackknife: Cannot divide quantity X in equal sized subgroups!") + print ("Choose another size for subgroups!") return PHI_jack, PHI_pseudo, PHI_sub else: # estimator of undisturbed spot check @@ -834,7 +833,7 @@ def checkZ4S(X, pick, zfac, checkwin, iplot): assert isinstance(X, Stream), "%s is not a stream object" % str(X) - print 'Check for spuriously picked S onset instead of P onset ...' + print ("Check for spuriously picked S onset instead of P onset ...") returnflag = 0 @@ -875,9 +874,9 @@ def checkZ4S(X, pick, zfac, checkwin, iplot): # vertical P-coda level must exceed horizontal P-coda level # zfac times encodalevel if zcodalevel < minsiglevel: - print 'checkZ4S: Maybe S onset? Skip this P pick!' + print ("checkZ4S: Maybe S onset? Skip this P pick!") else: - print 'checkZ4S: P onset passes checkZ4S test!' + print ("checkZ4S: P onset passes checkZ4S test!") returnflag = 1 if iplot > 1: