From fd6e4cb02af8674fe99542b9e1fab0d9841d87ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ludger=20K=C3=BCperkoch?= Date: Mon, 22 Jun 2015 11:07:22 +0200 Subject: [PATCH] Uses now UTCDateTime.timestamp as this is more efficient and shorter. --- pylot/core/pick/utils.py | 53 ++++++++++++++++++---------------------- 1 file changed, 24 insertions(+), 29 deletions(-) diff --git a/pylot/core/pick/utils.py b/pylot/core/pick/utils.py index 6bc8a106..0909a895 100644 --- a/pylot/core/pick/utils.py +++ b/pylot/core/pick/utils.py @@ -13,6 +13,7 @@ import matplotlib.pyplot as plt from obspy.core import Stream, UTCDateTime import warnings + def earllatepicker(X, nfac, TSNR, Pick1, iplot=None): ''' Function to derive earliest and latest possible pick after Diehl & Kissling (2009) @@ -65,8 +66,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None): #get earliest possible pick - #determine all zero crossings in signal window (demeaned) - zc = crossings_nonzero_all(x[isignal] - x[isignal].mean()) + #determine all zero crossings in signal window + zc = crossings_nonzero_all(x[isignal]) #calculate mean half period T0 of signal as the average of the T0 = np.mean(np.diff(zc)) * X[0].stats.delta #this is half wave length! #T0/4 is assumed as time difference between most likely and earliest possible pick! @@ -385,13 +386,13 @@ def getsignalwin(t, t1, tsignal): def wadaticheck(pickdic, dttolerance, iplot): ''' Function to calculate Wadati-diagram from given P and S onsets in order - to detect S pick outliers. If a certain S-P time deviates from regression - of S-P time the S pick is marked and down graded. + to detect S pick outliers. If a certain S-P time deviates by dttolerance + from regression of S-P time the S pick is marked and down graded. : param: pickdic, dictionary containing picks and quality parameters : type: dictionary - - : param: dttolerance, maximum adjusted deviation of S-P time from + + : param: dttolerance, maximum adjusted deviation of S-P time from S-P time regression : type: float @@ -405,7 +406,6 @@ def wadaticheck(pickdic, dttolerance, iplot): Ppicks = [] Spicks = [] SPtimes = [] - vpvs = [] for key in pickdic: if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4: # calculate S-P time @@ -413,65 +413,60 @@ def wadaticheck(pickdic, dttolerance, iplot): # add S-P time to dictionary pickdic[key]['SPt'] = spt # add P onsets and corresponding S-P times to list - UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,1,1,0,0,0) - UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(1970,1,1,0,0,0) - Ppicks.append(UTCPpick) - Spicks.append(UTCSpick) + UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) + UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) + Ppicks.append(UTCPpick.timestamp) + Spicks.append(UTCSpick.timestamp) SPtimes.append(spt) - vpvs.append(UTCPpick/UTCSpick) if len(SPtimes) >= 3: - # calculate slope + # calculate slope p1 = np.polyfit(Ppicks, SPtimes, 1) wdfit = np.polyval(p1, Ppicks) wfitflag = 0 - - # calculate average vp/vs ratio before check + + # calculate vp/vs ratio before check vpvsr = p1[0] + 1 print 'wadaticheck: Average Vp/Vs ratio before check:', vpvsr checkedPpicks = [] checkedSpicks = [] checkedSPtimes = [] - checkedvpvs = [] # calculate deviations from Wadati regression for key in pickdic: if pickdic[key].has_key('SPt'): ii = 0 - wddiff = abs(pickdic[key]['SPt'] - wdfit[ii]) + wddiff = abs(pickdic[key]['SPt'] - wdfit[ii]) ii += 1 # check, if deviation is larger than adjusted if wddiff >= dttolerance: - # mark onset and downgrade S-weight to 9 + # mark onset and downgrade S-weight to 9 # (not used anymore) marker = 'badWadatiCheck' pickdic[key]['S']['weight'] = 9 else: marker = 'goodWadatiCheck' - checkedPpick = UTCDateTime(pickdic[key]['P']['mpp']) - \ - UTCDateTime(1970,1,1,0,0,0) - checkedPpicks.append(checkedPpick) - checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) - \ - UTCDateTime(1970,1,1,0,0,0) - checkedSpicks.append(checkedSpick) + checkedPpick = UTCDateTime(pickdic[key]['P']['mpp']) + checkedPpicks.append(checkedPpick.timestamp) + checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) + checkedSpicks.append(checkedSpick.timestamp) checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp'] checkedSPtimes.append(checkedSPtime) - checkedvpvs.append(checkedPpick/checkedSpick) pickdic[key]['S']['marked'] = marker - # calculate new slope + # calculate new slope p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1) wdfit2 = np.polyval(p2, checkedPpicks) - # calculate average vp/vs ratio after check + # calculate vp/vs ratio after check cvpvsr = p2[0] + 1 print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr checkedonsets = pickdic - + else: print 'wadaticheck: Not enough S-P times available for reliable regression!' print 'Skip wadati check!' @@ -487,7 +482,7 @@ def wadaticheck(pickdic, dttolerance, iplot): f4, = plt.plot(checkedPpicks, wdfit2, 'g') plt.ylabel('S-P Times [s]') plt.xlabel('P Times [s]') - plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(old)=%5.2f, Vp/Vs(checked)=%5.2f' \ + plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f, Vp/Vs(checked)=%5.2f' \ % (len(SPtimes), vpvsr, cvpvsr)) plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', 'Reliable S-Picks', \ 'Wadati 2'], loc='best')