marcel/pylot
3
0
Fork 0
Code Issues 7 Pull Requests 1 Releases Wiki Activity

Marginal changes.

Browse Source
This commit is contained in:
Ludger Küperkoch 2015-07-01 15:31:50 +02:00
parent 5bb616ffc5
commit 3e81adfec6
1 changed files with 237 additions and 162 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

391
pylot/core/pick/utils.py
View File

@ -13,8 +13,6 @@ import scipy as sc
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from obspy.core import Stream, UTCDateTime from obspy.core import Stream, UTCDateTime
import warnings import warnings
import pdb
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None): def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
''' '''
@ -61,7 +59,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
ilup, = np.where(x[isignal] > nlevel) ilup, = np.where(x[isignal] > nlevel)
ildown, = np.where(x[isignal] < -nlevel) ildown, = np.where(x[isignal] < -nlevel)
if not ilup.size and not ildown.size: if not ilup.size and not ildown.size:
print 'earllatepicker: Signal lower than noise level!' print 'earllatepicker: Signal lower than noise level!'
print 'Skip this trace!' print 'Skip this trace!'
return LPick, EPick, PickError return LPick, EPick, PickError
il = min(np.min(ilup) if ilup.size else float('inf'), il = min(np.min(ilup) if ilup.size else float('inf'),
@ -188,11 +186,11 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
else: else:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]])) imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
if imax1 == 0: if imax1 == 0:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]])) imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
if imax1 == 0: if imax1 == 0:
print 'fmpicker: Zero crossings too close!' print 'fmpicker: Zero crossings too close!'
print 'Skip first motion determination!' print 'Skip first motion determination!'
return FM return FM
islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1])) islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
# calculate slope as polynomal fit of order 1 # calculate slope as polynomal fit of order 1
@ -230,11 +228,11 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
else: else:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]])) imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
if imax2 == 0: if imax2 == 0:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]])) imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
if imax1 == 0: if imax1 == 0:
print 'fmpicker: Zero crossings too close!' print 'fmpicker: Zero crossings too close!'
print 'Skip first motion determination!' print 'Skip first motion determination!'
return FM return FM
islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2])) islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
# calculate slope as polynomal fit of order 1 # calculate slope as polynomal fit of order 1
@ -257,6 +255,8 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
elif P1[0] > 0 and P2[0] <= 0: elif P1[0] > 0 and P2[0] <= 0:
FM = '+' FM = '+'
print 'fmpicker: Found polarity %s' % FM
if iplot > 1: if iplot > 1:
plt.figure(iplot) plt.figure(iplot)
plt.subplot(2, 1, 1) plt.subplot(2, 1, 1)
@ -301,71 +301,48 @@ def crossings_nonzero_all(data):
return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0] return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
def getSNR(st, TSNR, t0): def getSNR(X, TSNR, t1):
""" '''
returns the maximum signal to noise ratio SNR (also in dB) and the Function to calculate SNR of certain part of seismogram relative to
corresponding noise level for a given data stream ST ,initial time T0 and given time (onset) out of given noise and signal windows. A safety gap
time window parameter tuple TSNR between noise and signal part can be set. Returns SNR and SNR [dB] and
noiselevel.
:param: st, time series (seismogram) :param: X, time series (seismogram)
:type: `~obspy.core.stream.Stream` :type: `~obspy.core.stream.Stream`
:param: TSNR, length of time windows [s] around t0 (onset) used to determine
SNR :param: TSNR, length of time windows [s] around t1 (onset) used to determine SNR
:type: tuple (T_noise, T_gap, T_signal) :type: tuple (T_noise, T_gap, T_signal)
:param: t0, initial time (onset) from which noise and signal windows are calculated
:param: t1, initial time (onset) from which noise and signal windows are calculated
:type: float :type: float
:return: SNR, SNRdB, noiselevel '''
..examples: assert isinstance(X, Stream), "%s is not a stream object" % str(X)
>>> from obspy.core import read x = X[0].data
>>> st = read() t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
>>> result = getSNR(st, (6., .3, 3.), 4.67) X[0].stats.delta)
>>> print result
(5.1267717641040758, 7.0984398375666435, 132.89370192191919)
>>> result = getSNR(st, (8., .2, 5.), 4.67)
>>> print result
(4.645441835797703, 6.6702702677384131, 133.03562794665109)
"""
assert isinstance(st, Stream), "%s is not a stream object" % str(st) # get noise window
inoise = getnoisewin(t, t1, TSNR[0], TSNR[1])
SNR = None # get signal window
noiselevel = None isignal = getsignalwin(t, t1, TSNR[2])
if np.size(inoise) < 1:
print 'getSNR: Empty array inoise, check noise window!'
return
elif np.size(isignal) < 1:
print 'getSNR: Empty array isignal, check signal window!'
return
for tr in st: # demean over entire waveform
x = tr.data x = x - np.mean(x[inoise])
t = np.arange(0, tr.stats.npts / tr.stats.sampling_rate,
tr.stats.delta)
# get noise window
inoise = getnoisewin(t, t0, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, t0, TSNR[2])
if np.size(inoise) < 1:
print 'getSNR: Empty array inoise, check noise window!'
return
elif np.size(isignal) < 1:
print 'getSNR: Empty array isignal, check signal window!'
return
# demean over entire waveform
x = x - np.mean(x[inoise])
# calculate ratios
new_noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
signallevel = np.sqrt(np.mean(np.square(x[isignal])))
newSNR = signallevel / new_noiselevel
if not SNR or newSNR > SNR:
SNR = newSNR
noiselevel = new_noiselevel
if not SNR or not noiselevel:
raise ValueError('signal to noise ratio could not be calculated:\n'
'noiselevel: {0}\n'
'SNR: {1}'.format(noiselevel, SNR))
# calculate ratios
noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
signallevel = np.sqrt(np.mean(np.square(x[isignal])))
SNR = signallevel / noiselevel
SNRdB = 10 * np.log10(SNR) SNRdB = 10 * np.log10(SNR)
return SNR, SNRdB, noiselevel return SNR, SNRdB, noiselevel
@ -392,7 +369,7 @@ def getnoisewin(t, t1, tnoise, tgap):
# get noise window # get noise window
inoise, = np.where((t <= max([t1 - tgap, 0])) \ inoise, = np.where((t <= max([t1 - tgap, 0])) \
& (t >= max([t1 - tnoise - tgap, 0]))) & (t >= max([t1 - tnoise - tgap, 0])))
if np.size(inoise) < 1: if np.size(inoise) < 1:
print 'getnoisewin: Empty array inoise, check noise window!' print 'getnoisewin: Empty array inoise, check noise window!'
@ -416,7 +393,7 @@ def getsignalwin(t, t1, tsignal):
# get signal window # get signal window
isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \ isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
& (t >= t1)) & (t >= t1))
if np.size(isignal) < 1: if np.size(isignal) < 1:
print 'getsignalwin: Empty array isignal, check signal window!' print 'getsignalwin: Empty array isignal, check signal window!'
@ -457,7 +434,7 @@ def getResolutionWindow(snr):
else: else:
time_resolution = res_wins['HRW'] time_resolution = res_wins['HRW']
return time_resolution / 2 return time_resolution/2
def wadaticheck(pickdic, dttolerance, iplot): def wadaticheck(pickdic, dttolerance, iplot):
@ -485,21 +462,22 @@ def wadaticheck(pickdic, dttolerance, iplot):
SPtimes = [] SPtimes = []
for key in pickdic: for key in pickdic:
if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4: if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
# calculate S-P time # calculate S-P time
spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp'] spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
# add S-P time to dictionary # add S-P time to dictionary
pickdic[key]['SPt'] = spt pickdic[key]['SPt'] = spt
# add P onsets and corresponding S-P times to list # add P onsets and corresponding S-P times to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
Ppicks.append(UTCPpick.timestamp) Ppicks.append(UTCPpick.timestamp)
Spicks.append(UTCSpick.timestamp) Spicks.append(UTCSpick.timestamp)
SPtimes.append(spt) SPtimes.append(spt)
if len(SPtimes) >= 3: if len(SPtimes) >= 3:
# calculate slope # calculate slope
p1 = np.polyfit(Ppicks, SPtimes, 1) p1 = np.polyfit(Ppicks, SPtimes, 1)
wdfit = np.polyval(p1, Ppicks) wdfit = np.polyval(p1, Ppicks)
wfitflag = 0 wfitflag = 0
# calculate vp/vs ratio before check # calculate vp/vs ratio before check
@ -523,50 +501,48 @@ def wadaticheck(pickdic, dttolerance, iplot):
pickdic[key]['S']['weight'] = 9 pickdic[key]['S']['weight'] = 9
else: else:
marker = 'goodWadatiCheck' marker = 'goodWadatiCheck'
checkedPpick = UTCDateTime(pickdic[key]['P']['mpp']) checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
checkedPpicks.append(checkedPpick.timestamp) checkedPpicks.append(checkedPpick.timestamp)
checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
checkedSpicks.append(checkedSpick.timestamp) checkedSpicks.append(checkedSpick.timestamp)
checkedSPtime = pickdic[key]['S']['mpp'] - \ checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
pickdic[key]['P']['mpp']
checkedSPtimes.append(checkedSPtime) checkedSPtimes.append(checkedSPtime)
pickdic[key]['S']['marked'] = marker pickdic[key]['S']['marked'] = marker
if len(checkedPpicks) >= 3: if len(checkedPpicks) >= 3:
# calculate new slope # calculate new slope
p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1) p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
wdfit2 = np.polyval(p2, checkedPpicks) wdfit2 = np.polyval(p2, checkedPpicks)
# calculate vp/vs ratio after check # calculate vp/vs ratio after check
cvpvsr = p2[0] + 1 cvpvsr = p2[0] + 1
print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
else: else:
print 'wadatacheck: Not enough checked S-P times available!' print 'wadatacheck: Not enough checked S-P times available!'
print 'Skip Wadati check!' print 'Skip Wadati check!'
checkedonsets = pickdic checkedonsets = pickdic
else: else:
print 'wadaticheck: Not enough S-P times available for reliable regression!' print 'wadaticheck: Not enough S-P times available for reliable regression!'
print 'Skip wadati check!' print 'Skip wadati check!'
wfitflag = 1 wfitflag = 1
iplot = 2 iplot=2
# plot results # plot results
if iplot > 1: if iplot > 1:
plt.figure(iplot) plt.figure(iplot)
f1, = plt.plot(Ppicks, SPtimes, 'ro') f1, = plt.plot(Ppicks, SPtimes, 'ro')
if wfitflag == 0: if wfitflag == 0:
f2, = plt.plot(Ppicks, wdfit, 'k') f2, = plt.plot(Ppicks, wdfit, 'k')
f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko') f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
f4, = plt.plot(checkedPpicks, wdfit2, 'g') f4, = plt.plot(checkedPpicks, wdfit2, 'g')
plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \ plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr)) 'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', \ plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', \
'Reliable S-Picks', 'Wadati 2'], 'Reliable S-Picks', 'Wadati 2'], loc='best')
loc='best')
else: else:
plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes)) plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
plt.ylabel('S-P Times [s]') plt.ylabel('S-P Times [s]')
plt.xlabel('P Times [s]') plt.xlabel('P Times [s]')
@ -626,12 +602,12 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
# calculate minimum adjusted signal level # calculate minimum adjusted signal level
minsiglevel = max(e[inoise]) * nfac minsiglevel = max(e[inoise]) * nfac
# minimum adjusted number of samples over minimum signal level # minimum adjusted number of samples over minimum signal level
minnum = len(isignal) * minpercent / 100 minnum = len(isignal) * minpercent/100
# get number of samples above minimum adjusted signal level # get number of samples above minimum adjusted signal level
numoverthr = len(np.where(e[isignal] >= minsiglevel)[0]) numoverthr = len(np.where(e[isignal] >= minsiglevel)[0])
if numoverthr >= minnum: if numoverthr >= minnum:
print 'checksignallength: Signal reached required length.' print 'checksignallength: Signal reached required length.'
returnflag = 1 returnflag = 1
else: else:
print 'checksignallength: Signal shorter than required minimum signal length!' print 'checksignallength: Signal shorter than required minimum signal length!'
@ -640,18 +616,17 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
if iplot == 2: if iplot == 2:
plt.figure(iplot) plt.figure(iplot)
p1, = plt.plot(t, x, 'k') p1, = plt.plot(t,x, 'k')
p2, = plt.plot(t[inoise], e[inoise], 'c') p2, = plt.plot(t[inoise], e[inoise], 'c')
p3, = plt.plot(t[isignal], e[isignal], 'r') p3, = plt.plot(t[isignal],e[isignal], 'r')
p2, = plt.plot(t[inoise], e[inoise]) p2, = plt.plot(t[inoise], e[inoise])
p3, = plt.plot(t[isignal], e[isignal], 'r') p3, = plt.plot(t[isignal],e[isignal], 'r')
p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]], \ p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \
[minsiglevel, minsiglevel], 'g') [minsiglevel, minsiglevel], 'g')
p5, = plt.plot([pick, pick], [min(x), max(x)], 'b', 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', \ plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \
'Envelope Signal Window', 'Envelope Signal Window', 'Minimum Signal Level', \
'Minimum Signal Level', \ 'Onset'], loc='best')
'Onset'], loc='best')
plt.xlabel('Time [s] since %s' % X[0].stats.starttime) plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
plt.ylabel('Counts') plt.ylabel('Counts')
plt.title('Check for Signal Length, Station %s' % X[0].stats.station) plt.title('Check for Signal Length, Station %s' % X[0].stats.station)
@ -687,14 +662,14 @@ def checkPonsets(pickdic, dttolerance, iplot):
stations = [] stations = []
for key in pickdic: for key in pickdic:
if pickdic[key]['P']['weight'] < 4: if pickdic[key]['P']['weight'] < 4:
# add P onsets to list # add P onsets to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
Ppicks.append(UTCPpick.timestamp) Ppicks.append(UTCPpick.timestamp)
stations.append(key) stations.append(key)
# apply jackknife bootstrapping on variance of P onsets # apply jackknife bootstrapping on variance of P onsets
print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...' print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...'
[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1) [xjack,PHI_pseudo,PHI_sub] = jackknife(Ppicks, 'VAR', 1)
# get pseudo variances smaller than average variances # get pseudo variances smaller than average variances
# these picks passed jackknife test # these picks passed jackknife test
ij = np.where(PHI_pseudo <= xjack) ij = np.where(PHI_pseudo <= xjack)
@ -713,41 +688,40 @@ def checkPonsets(pickdic, dttolerance, iplot):
badstations = np.array(stations)[ibad] badstations = np.array(stations)[ibad]
print 'checkPonset: Skipped %d P onsets out of %d' % (len(badstations) \ print 'checkPonset: Skipped %d P onsets out of %d' % (len(badstations) \
+ len(badjkstations), + len(badjkstations), len(stations))
len(stations))
goodmarker = 'goodPonsetcheck' goodmarker = 'goodPonsetcheck'
badmarker = 'badPonsetcheck' badmarker = 'badPonsetcheck'
badjkmarker = 'badjkcheck' badjkmarker = 'badjkcheck'
for i in range(0, len(goodstations)): for i in range(0, len(goodstations)):
# mark P onset as checked and keep P weight # mark P onset as checked and keep P weight
pickdic[goodstations[i]]['P']['marked'] = goodmarker pickdic[goodstations[i]]['P']['marked'] = goodmarker
for i in range(0, len(badstations)): for i in range(0, len(badstations)):
# mark P onset and downgrade P weight to 9 # mark P onset and downgrade P weight to 9
# (not used anymore) # (not used anymore)
pickdic[badstations[i]]['P']['marked'] = badmarker pickdic[badstations[i]]['P']['marked'] = badmarker
pickdic[badstations[i]]['P']['weight'] = 9 pickdic[badstations[i]]['P']['weight'] = 9
for i in range(0, len(badjkstations)): for i in range(0, len(badjkstations)):
# mark P onset and downgrade P weight to 9 # mark P onset and downgrade P weight to 9
# (not used anymore) # (not used anymore)
pickdic[badjkstations[i]]['P']['marked'] = badjkmarker pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
pickdic[badjkstations[i]]['P']['weight'] = 9 pickdic[badjkstations[i]]['P']['weight'] = 9
checkedonsets = pickdic checkedonsets = pickdic
iplot = 2 iplot = 2
if iplot > 1: if iplot > 1:
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14) p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14) p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14)
p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', \ p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', \
linewidth=2) linewidth=2)
for i in range(0, len(Ppicks)): for i in range(0, len(Ppicks)):
plt.text(i, Ppicks[i] + 0.2, stations[i]) plt.text(i, Ppicks[i] + 0.2, stations[i])
plt.xlabel('Number of P Picks') plt.xlabel('Number of P Picks')
plt.ylabel('Onset Time [s] from 1.1.1970') plt.ylabel('Onset Time [s] from 1.1.1970')
plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'], \ plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'], \
loc='best') loc='best')
plt.title('Check P Onsets') plt.title('Check P Onsets')
plt.show() plt.show()
raw_input() raw_input()
@ -782,44 +756,145 @@ def jackknife(X, phi, h):
g = len(X) / h g = len(X) / h
if type(g) is not int: if type(g) is not int:
print 'jackknife: Cannot divide quantity X in equal sized subgroups!' print 'jackknife: Cannot divide quantity X in equal sized subgroups!'
print 'Choose another size for subgroups!' print 'Choose another size for subgroups!'
return PHI_jack, PHI_pseudo, PHI_sub return PHI_jack, PHI_pseudo, PHI_sub
else: else:
# estimator of undisturbed spot check # estimator of undisturbed spot check
if phi == 'MEA': if phi == 'MEA':
phi_sc = np.mean(X) phi_sc = np.mean(X)
elif phi == 'VAR': elif phi == 'VAR':
phi_sc = np.var(X) phi_sc = np.var(X)
elif phi == 'MED': elif phi == 'MED':
phi_sc = np.median(X) phi_sc = np.median(X)
# estimators of subgroups # estimators of subgroups
PHI_pseudo = [] PHI_pseudo = []
PHI_sub = [] PHI_sub = []
for i in range(0, g - 1): for i in range(0, g - 1):
# subgroup i, remove i-th sample # subgroup i, remove i-th sample
xx = X[:] xx = X[:]
del xx[i] del xx[i]
# calculate estimators of disturbed spot check # calculate estimators of disturbed spot check
if phi == 'MEA': if phi == 'MEA':
phi_sub = np.mean(xx) phi_sub = np.mean(xx)
elif phi == 'VAR': elif phi == 'VAR':
phi_sub = np.var(xx) phi_sub = np.var(xx)
elif phi == 'MED': elif phi == 'MED':
phi_sub = np.median(xx) phi_sub = np.median(xx)
PHI_sub.append(phi_sub) PHI_sub.append(phi_sub)
# pseudo values # pseudo values
phi_pseudo = g * phi_sc - ((g - 1) * phi_sub) phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
PHI_pseudo.append(phi_pseudo) PHI_pseudo.append(phi_pseudo)
# jackknife estimator # jackknife estimator
PHI_jack = np.mean(PHI_pseudo) PHI_jack = np.mean(PHI_pseudo)
return PHI_jack, PHI_pseudo, PHI_sub return PHI_jack, PHI_pseudo, PHI_sub
def checkZ4S(X, pick, zfac, checkwin, iplot):
'''
Function to compare energy content of vertical trace with
energy content of horizontal traces to detect spuriously
picked S onsets instead of P onsets. Usually, P coda shows
larger longitudal energy on vertical trace than on horizontal
traces, where the transversal energy is larger within S coda.
Be careful: there are special circumstances, where this is not
the case!
: param: X, fitered(!) time series, three traces
: type: `~obspy.core.stream.Stream`
: param: pick, initial (AIC) P onset time
: type: float
: param: zfac, factor for threshold determination,
vertical energy must exceed coda level times zfac
to declare a pick as P onset
: type: float
: param: checkwin, window length [s] for calculating P-coda
energy content
: type: float
: param: iplot, if iplot > 1, energy content and threshold
are shown
: type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print 'Check for spuriously picked S onset instead of P onset ...'
returnflag = 0
# split components
zdat = X.select(component="Z")
edat = X.select(component="E")
if len(edat) == 0: # check for other components
edat = X.select(component="2")
ndat = X.select(component="N")
if len(ndat) == 0: # check for other components
ndat = X.select(component="1")
z = zdat[0].data
tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
zdat[0].stats.delta)
# calculate RMS trace from vertical component
absz = np.sqrt(np.power(z, 2))
# calculate RMS trace from both horizontal traces
# make sure, both traces have equal lengths
lene = len(edat[0].data)
lenn = len(ndat[0].data)
minlen = min([lene, lenn])
absen = np.sqrt(np.power(edat[0].data[0:minlen - 1], 2) \
+ np.power(ndat[0].data[0:minlen - 1], 2))
# get signal window
isignal = getsignalwin(tz, pick, checkwin)
# calculate energy levels
zcodalevel = max(absz[isignal])
encodalevel = max(absen[isignal])
# calculate threshold
minsiglevel = encodalevel * zfac
# 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!'
else:
print 'checkZ4S: P onset passed checkZ4S test!'
returnflag = 1
if iplot > 1:
te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
edat[0].stats.delta)
tn = np.arange(0, ndat[0].stats.npts / ndat[0].stats.sampling_rate,
ndat[0].stats.delta)
plt.plot(tz, z / max(z), 'k')
plt.plot(tz[isignal], z[isignal] / max(z), 'r')
plt.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
plt.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r')
plt.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
plt.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r')
plt.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]], \
[minsiglevel / max(z), minsiglevel / max(z)], 'g', \
linewidth=2)
plt.xlabel('Time [s] since %s' % zdat[0].stats.starttime)
plt.ylabel('Normalized Counts')
plt.yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel, \
ndat[0].stats.channel])
plt.title('CheckZ4S, Station %s' % zdat[0].stats.station)
plt.show()
raw_input()
return returnflag
if __name__ == '__main__': if __name__ == '__main__':
import doctest import doctest
doctest.testmod() doctest.testmod()