marcel/pylot
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Merge branch 'develop' of 134.147.164.251:/data/git/pylot into develop

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Sebastian Wehling-Benatelli
2015-06-28 19:35:08 +02:00
parent a854cf2762 90bf6cb99e
commit a46fb88282
5 changed files with 189 additions and 31 deletions
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2
pylot/RELEASE-VERSION
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@@ -1 +1 @@
694a-dirty
1abc-dirty

170
pylot/core/pick/utils.py
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@@ -13,7 +13,7 @@ import scipy as sc
import matplotlib.pyplot as plt
from obspy.core import Stream, UTCDateTime
import warnings
import pdb
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
'''
Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
@@ -155,7 +155,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
# get next zero crossing after most likely pick
# get zero crossings after most likely pick
# initial onset is assumed to be the first zero crossing
# first from unfiltered trace
zc1 = []
@@ -199,7 +199,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
datafit1 = np.polyval(P1, xslope1)
# now using filterd trace
# next zero crossing after most likely pick
# next zero crossings after most likely pick
zc2 = []
zc2.append(Pick)
index2 = []
@@ -492,7 +492,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
ii += 1
# check, if deviation is larger than adjusted
if wddiff >= dttolerance:
if wddiff > dttolerance:
# mark onset and downgrade S-weight to 9
# (not used anymore)
marker = 'badWadatiCheck'
@@ -526,7 +526,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
print 'wadaticheck: Not enough S-P times available for reliable regression!'
print 'Skip wadati check!'
wfitflag = 1
iplot=2
# plot results
if iplot > 1:
plt.figure(iplot)
@@ -615,11 +615,13 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
if iplot == 2:
plt.figure(iplot)
p1, = plt.plot(t,x, 'k')
p2, = plt.plot(t[inoise], e[inoise], 'c')
p3, = plt.plot(t[isignal],e[isignal], 'r')
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')
p5, = plt.plot([pick, pick], [min(x), max(x)], 'c')
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', \
'Onset'], loc='best')
@@ -633,6 +635,162 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
return returnflag
def checkPonsets(pickdic, dttolerance, iplot):
'''
Function to check statistics of P-onset times: Control deviation from
median (maximum adjusted deviation = dttolerance) and apply pseudo-
bootstrapping jackknife.
: param: pickdic, dictionary containing picks and quality parameters
: type: dictionary
: param: dttolerance, maximum adjusted deviation of P-onset time from
median of all P onsets
: type: float
: param: iplot, if iplot > 1, Wadati diagram is shown
: type: int
'''
checkedonsets = pickdic
# search for good quality P picks
Ppicks = []
stations = []
for key in pickdic:
if pickdic[key]['P']['weight'] < 4:
# add P onsets to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
Ppicks.append(UTCPpick.timestamp)
stations.append(key)
# apply jackknife bootstrapping on variance of P onsets
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
# these picks passed jackknife test
ij = np.where(PHI_pseudo <= xjack)
# these picks did not pass jackknife test
badjk = np.where(PHI_pseudo > xjack)
badjkstations = np.array(stations)[badjk]
# calculate median from these picks
pmedian = np.median(np.array(Ppicks)[ij])
# find picks that deviate less than dttolerance from median
ii = np.where(abs(np.array(Ppicks)[ij] - pmedian) <= dttolerance)
jj = np.where(abs(np.array(Ppicks)[ij] - pmedian) > dttolerance)
igood = ij[0][ii]
ibad = ij[0][jj]
goodstations = np.array(stations)[igood]
badstations = np.array(stations)[ibad]
print 'checkPonset: Skipped %d P onsets out of %d' % (len(badstations) \
+ len(badjkstations), len(stations))
goodmarker = 'goodPonsetcheck'
badmarker = 'badPonsetcheck'
badjkmarker = 'badjkcheck'
for i in range(0, len(goodstations)):
# mark P onset as checked and keep P weight
pickdic[goodstations[i]]['P']['marked'] = goodmarker
for i in range(0, len(badstations)):
# mark P onset and downgrade P weight to 9
# (not used anymore)
pickdic[badstations[i]]['P']['marked'] = badmarker
pickdic[badstations[i]]['P']['weight'] = 9
for i in range(0, len(badjkstations)):
# mark P onset and downgrade P weight to 9
# (not used anymore)
pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
pickdic[badjkstations[i]]['P']['weight'] = 9
checkedonsets = pickdic
iplot = 2
if iplot > 1:
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14)
p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', \
linewidth=2)
for i in range(0, len(Ppicks)):
plt.text(i, Ppicks[i] + 0.2, stations[i])
plt.xlabel('Number of P Picks')
plt.ylabel('Onset Time [s] from 1.1.1970')
plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'], \
loc='best')
plt.title('Check P Onsets')
plt.show()
raw_input()
return checkedonsets
def jackknife(X, phi, h):
'''
Function to calculate the Jackknife Estimator for a given quantity,
special type of boot strapping. Returns the jackknife estimator PHI_jack
the pseudo values PHI_pseudo and the subgroup parameters PHI_sub.
: param: X, given quantity
: type: list
: param: phi, chosen estimator, choose between:
"MED" for median
"MEA" for arithmetic mean
"VAR" for variance
: type: string
: param: h, size of subgroups, optinal, default = 1
: type: integer
'''
PHI_jack = None
PHI_pseudo = None
PHI_sub = None
# determine number of subgroups
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!'
return PHI_jack, PHI_pseudo, PHI_sub
else:
# estimator of undisturbed spot check
if phi == 'MEA':
phi_sc = np.mean(X)
elif phi == 'VAR':
phi_sc = np.var(X)
elif phi == 'MED':
phi_sc = np.median(X)
# estimators of subgroups
PHI_pseudo = []
PHI_sub = []
for i in range(0, g - 1):
# subgroup i, remove i-th sample
xx = X[:]
del xx[i]
# calculate estimators of disturbed spot check
if phi == 'MEA':
phi_sub = np.mean(xx)
elif phi == 'VAR':
phi_sub = np.var(xx)
elif phi == 'MED':
phi_sub = np.median(xx)
PHI_sub.append(phi_sub)
# pseudo values
phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
PHI_pseudo.append(phi_pseudo)
# jackknife estimator
PHI_jack = np.mean(PHI_pseudo)
return PHI_jack, PHI_pseudo, PHI_sub
if __name__ == '__main__':
import doctest
doctest.testmod()