Merge branch 'develop' of 134.147.164.251:/data/git/pylot into develop
This commit is contained in:
commit
a46fb88282
18
autoPyLoT.py
18
autoPyLoT.py
@ -12,7 +12,7 @@ from pylot.core.util import _getVersionString
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from pylot.core.read import Data, AutoPickParameter
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from pylot.core.pick.run_autopicking import run_autopicking
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from pylot.core.util.structure import DATASTRUCTURE
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from pylot.core.pick.utils import wadaticheck
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from pylot.core.pick.utils import wadaticheck, checkPonsets
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import pdb
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__version__ = _getVersionString()
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@ -51,6 +51,7 @@ def autoPyLoT(inputfile):
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# get some parameters for quality control from
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# parameter input file (usually autoPyLoT.in).
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wdttolerance = parameter.getParam('wdttolerance')
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mdttolerance = parameter.getParam('mdttolerance')
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iplot = parameter.getParam('iplot')
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data = Data()
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@ -105,10 +106,11 @@ def autoPyLoT(inputfile):
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allonsets[station] = picks
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# quality control
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# jackknife on P onset times
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# median check and jackknife on P onset times
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checkedonsetsjk = checkPonsets(allonsets, mdttolerance, iplot)
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# check S-P times (Wadati)
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checkedonsets = wadaticheck(allonsets, wdttolerance, iplot)
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# jackknife on S onset times
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checkedonsetwd = wadaticheck(checkedonsetsjk, wdttolerance, iplot)
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print '------------------------------------------'
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print '-----Finished event %s!-----' % event
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print '------------------------------------------'
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@ -128,6 +130,7 @@ def autoPyLoT(inputfile):
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station = wfdat[0].stats.station
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allonsets = {station: picks}
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for i in range(len(wfdat)):
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#for i in range(0,10):
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stationID = wfdat[i].stats.station
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#check if station has already been processed
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if stationID not in procstats:
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@ -143,10 +146,11 @@ def autoPyLoT(inputfile):
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allonsets[station] = picks
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# quality control
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#jackknife on P onset times
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# median check and jackknife on P onset times
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checkedonsetsjk = checkPonsets(allonsets, mdttolerance, iplot)
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# check S-P times (Wadati)
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checkedonsets = wadaticheck(allonsets, wdttolerance, iplot)
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#jackknife on S onset times
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checkedonsetswd = wadaticheck(checkedonsetsjk, wdttolerance, iplot)
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print '------------------------------------------'
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print '-------Finished event %s!-------' % parameter.getParam('eventID')
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print '------------------------------------------'
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@ -90,10 +90,8 @@ ARH #algoS# %choose algorithm for S-onset
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70 #minpercent# %required percentage of samples higher than threshold
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#check for spuriously picked S-onsets#
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3.0 #zfac# %P-amplitude must exceed zfac times RMS-S amplitude
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#jackknife-processing for P-picks#
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3 #thresholdweight#%minimum required weight of picks
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3 #dttolerance# %maximum allowed deviation of P picks from median [s]
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4 #minstats# %minimum number of stations with reliable P picks
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#check statistics of P onsets#
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2.5 #mdttolerance# %maximum allowed deviation of P picks from median [s]
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#wadati check#
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0.8 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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0.5 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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@ -36,7 +36,7 @@ HYPOSAT #locrt# %location routine used ("HYPO
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3 10 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
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3 12 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
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3 8 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
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3 6 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
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3 6 #bph2# %lower/upper corner freq. of second band pass filter H-comp. [Hz]
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#special settings for calculating CF#
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%!!Be careful when editing the following!!
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#Z-component#
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@ -49,10 +49,10 @@ HOS #algoP# %choose algorithm for P-onset
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0.6 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
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0.2 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
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0.001 #addnoise# %add noise to seismogram for stable AR prediction
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4 0.2 2.0 1.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
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4 #pickwinP# %for initial AIC and refined pick, length of P-pick window [s]
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5 0.2 3.0 1.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
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3 #pickwinP# %for initial AIC and refined pick, length of P-pick window [s]
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8 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
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3.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
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1.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
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0.3 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
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0.3 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
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1.3 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
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@ -88,10 +88,8 @@ ARH #algoS# %choose algorithm for S-onset
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60 #minpercent# %required percentage of samples higher than threshold
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#check for spuriously picked S-onsets#
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3.0 #zfac# %P-amplitude must exceed zfac times RMS-S amplitude
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#jackknife-processing for P-picks#
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3 #thresholdweight#%minimum required weight of picks
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3 #dttolerance# %maximum allowed deviation of P picks from median [s]
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4 #minstats# %minimum number of stations with reliable P picks
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#check statistics of P onsets#
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35 #mdttolerance# %maximum allowed deviation of P picks from median [s]
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#wadati check#
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1.5 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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2.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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@ -1 +1 @@
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694a-dirty
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1abc-dirty
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@ -13,7 +13,7 @@ import scipy as sc
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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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@ -155,7 +155,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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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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# get zero crossings 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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@ -199,7 +199,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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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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# next zero crossings 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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@ -492,7 +492,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
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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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if wddiff > dttolerance:
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# mark onset and downgrade S-weight to 9
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# (not used anymore)
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marker = 'badWadatiCheck'
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@ -526,7 +526,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
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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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# plot results
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if iplot > 1:
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plt.figure(iplot)
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@ -615,11 +615,13 @@ 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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p2, = plt.plot(t[inoise], e[inoise], 'c')
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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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p5, = plt.plot([pick, pick], [min(x), max(x)], 'c')
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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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@ -633,6 +635,162 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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return returnflag
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def checkPonsets(pickdic, dttolerance, iplot):
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'''
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Function to check statistics of P-onset times: Control deviation from
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median (maximum adjusted deviation = dttolerance) and apply pseudo-
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bootstrapping jackknife.
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: param: pickdic, dictionary containing picks and quality parameters
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: type: dictionary
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: param: dttolerance, maximum adjusted deviation of P-onset time from
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median of all P onsets
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: type: float
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: param: iplot, if iplot > 1, Wadati diagram is shown
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: type: int
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'''
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checkedonsets = pickdic
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# search for good quality P picks
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Ppicks = []
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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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# 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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# 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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# these picks did not pass jackknife test
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badjk = np.where(PHI_pseudo > xjack)
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badjkstations = np.array(stations)[badjk]
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# calculate median from these picks
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pmedian = np.median(np.array(Ppicks)[ij])
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# find picks that deviate less than dttolerance from median
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ii = np.where(abs(np.array(Ppicks)[ij] - pmedian) <= dttolerance)
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jj = np.where(abs(np.array(Ppicks)[ij] - pmedian) > dttolerance)
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igood = ij[0][ii]
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ibad = ij[0][jj]
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goodstations = np.array(stations)[igood]
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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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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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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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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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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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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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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.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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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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special type of boot strapping. Returns the jackknife estimator PHI_jack
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the pseudo values PHI_pseudo and the subgroup parameters PHI_sub.
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: param: X, given quantity
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: type: list
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: param: phi, chosen estimator, choose between:
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"MED" for median
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"MEA" for arithmetic mean
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"VAR" for variance
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: type: string
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: param: h, size of subgroups, optinal, default = 1
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: type: integer
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'''
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PHI_jack = None
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PHI_pseudo = None
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PHI_sub = None
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# determine number of subgroups
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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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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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elif phi == 'VAR':
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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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# 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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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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