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clean-up to meet coding conventions

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Sebastian Wehling-Benatelli 2015-06-29 16:14:11 +02:00
parent a46fb88282
commit 0fcd6fab9d
1 changed files with 108 additions and 102 deletions
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202
pylot/core/pick/utils.py
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@ -14,6 +14,8 @@ import matplotlib.pyplot as plt
from obspy.core import Stream, UTCDateTime from obspy.core import Stream, UTCDateTime
import warnings import warnings
import pdb import pdb
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None): def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
''' '''
Function to derive earliest and latest possible pick after Diehl & Kissling (2009) Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
@ -59,7 +61,7 @@ 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'),
@ -186,11 +188,11 @@ 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
@ -228,11 +230,11 @@ 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
@ -367,7 +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!'
@ -391,7 +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!'
@ -432,7 +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):
@ -460,22 +462,21 @@ 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
@ -499,48 +500,50 @@ 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'] - pickdic[key]['P']['mpp'] checkedSPtime = pickdic[key]['S']['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'], loc='best') 'Reliable S-Picks', 'Wadati 2'],
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]')
@ -600,12 +603,12 @@ 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!'
@ -614,17 +617,18 @@ 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', 'Minimum Signal Level', \ 'Envelope Signal Window',
'Onset'], loc='best') 'Minimum Signal Level', \
'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)
@ -660,14 +664,14 @@ 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)
@ -686,46 +690,48 @@ 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(stations)) + len(badjkstations),
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()
return checkedonsets return checkedonsets
def jackknife(X, phi, h): def jackknife(X, phi, h):
''' '''
Function to calculate the Jackknife Estimator for a given quantity, Function to calculate the Jackknife Estimator for a given quantity,
@ -753,44 +759,44 @@ 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
if __name__ == '__main__': if __name__ == '__main__':
import doctest import doctest
doctest.testmod() doctest.testmod()