marcel/pylot
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reformat code (fix indentation and python 3.x issues)

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Sebastian Wehling-Benatelli 2015-09-28 10:57:48 +02:00
parent 3dc9bb3d06
commit 4bd92565b9
1 changed files with 183 additions and 183 deletions
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366
pylot/core/pick/Picker.py
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@ -147,7 +147,7 @@ class AICPicker(AutoPicking):
def calcPick(self): def calcPick(self):
print 'AICPicker: Get initial onset time (pick) from AIC-CF ...' print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
self.Pick = None self.Pick = None
self.slope = None self.slope = None
@ -155,7 +155,7 @@ class AICPicker(AutoPicking):
#find NaN's #find NaN's
nn = np.isnan(self.cf) nn = np.isnan(self.cf)
if len(nn) > 1: if len(nn) > 1:
self.cf[nn] = 0 self.cf[nn] = 0
#taper AIC-CF to get rid off side maxima #taper AIC-CF to get rid off side maxima
tap = np.hanning(len(self.cf)) tap = np.hanning(len(self.cf))
aic = tap * self.cf + max(abs(self.cf)) aic = tap * self.cf + max(abs(self.cf))
@ -163,15 +163,15 @@ class AICPicker(AutoPicking):
ismooth = int(round(self.Tsmooth / self.dt)) ismooth = int(round(self.Tsmooth / self.dt))
aicsmooth = np.zeros(len(aic)) aicsmooth = np.zeros(len(aic))
if len(aic) < ismooth: if len(aic) < ismooth:
print 'AICPicker: Tsmooth larger than CF!' print('AICPicker: Tsmooth larger than CF!')
return return
else: else:
for i in range(1, len(aic)): for i in range(1, len(aic)):
if i > ismooth: if i > ismooth:
ii1 = i - ismooth ii1 = i - ismooth
aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
else: else:
aicsmooth[i] = np.mean(aic[1 : i]) aicsmooth[i] = np.mean(aic[1 : i])
#remove offset #remove offset
offset = abs(min(aic) - min(aicsmooth)) offset = abs(min(aic) - min(aicsmooth))
aicsmooth = aicsmooth - offset aicsmooth = aicsmooth - offset
@ -180,7 +180,7 @@ class AICPicker(AutoPicking):
#find NaN's #find NaN's
nn = np.isnan(diffcf) nn = np.isnan(diffcf)
if len(nn) > 1: if len(nn) > 1:
diffcf[nn] = 0 diffcf[nn] = 0
#taper CF to get rid off side maxima #taper CF to get rid off side maxima
tap = np.hanning(len(diffcf)) tap = np.hanning(len(diffcf))
diffcf = tap * diffcf * max(abs(aicsmooth)) diffcf = tap * diffcf * max(abs(aicsmooth))
@ -189,104 +189,104 @@ class AICPicker(AutoPicking):
#find minimum in AIC-CF front of maximum #find minimum in AIC-CF front of maximum
lpickwindow = int(round(self.PickWindow / self.dt)) lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1): for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if aicsmooth[i - 1] >= aicsmooth[i]: if aicsmooth[i - 1] >= aicsmooth[i]:
self.Pick = self.Tcf[i] self.Pick = self.Tcf[i]
break break
#if no minimum could be found: #if no minimum could be found:
#search in 1st derivative of AIC-CF #search in 1st derivative of AIC-CF
if self.Pick is None: if self.Pick is None:
for i in range(icfmax -1, max([icfmax -lpickwindow, 2]), -1): for i in range(icfmax -1, max([icfmax -lpickwindow, 2]), -1):
if diffcf[i -1] >= diffcf[i]: if diffcf[i -1] >= diffcf[i]:
self.Pick = self.Tcf[i] self.Pick = self.Tcf[i]
break break
# quality assessment using SNR and slope from CF # quality assessment using SNR and slope from CF
if self.Pick is not None: if self.Pick is not None:
# get noise window # get noise window
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1]) inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
# check, if these are counts or m/s, important for slope estimation! # check, if these are counts or m/s, important for slope estimation!
# this is quick and dirty, better solution? # this is quick and dirty, better solution?
if max(self.Data[0].data < 1e-3): if max(self.Data[0].data < 1e-3):
self.Data[0].data = self.Data[0].data * 1000000 self.Data[0].data = self.Data[0].data * 1000000
# get signal window # get signal window
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2]) isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
# calculate SNR from CF # calculate SNR from CF
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / max(abs(aic[inoise] \ self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
- np.mean(aic[inoise]))) max(abs(aic[inoise] - np.mean(aic[inoise])))
# calculate slope from CF after initial pick # calculate slope from CF after initial pick
# get slope window # get slope window
tslope = self.TSNR[3] #slope determination window tslope = self.TSNR[3] #slope determination window
islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \ islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
& (self.Tcf >= self.Pick)) & (self.Tcf >= self.Pick))
# find maximum within slope determination window # find maximum within slope determination window
# 'cause slope should be calculated up to first local minimum only! # 'cause slope should be calculated up to first local minimum only!
imax = np.argmax(self.Data[0].data[islope]) imax = np.argmax(self.Data[0].data[islope])
if imax == 0: if imax == 0:
print 'AICPicker: Maximum for slope determination right at the beginning of the window!' print('AICPicker: Maximum for slope determination right at the beginning of the window!')
print 'Choose longer slope determination window!' print('Choose longer slope determination window!')
if self.iplot > 1: if self.iplot > 1:
p = plt.figure(self.iplot) p = plt.figure(self.iplot)
x = self.Data[0].data x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k') p1, = plt.plot(self.Tcf, x / max(x), 'k')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r') p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF']) plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([]) plt.yticks([])
plt.title(self.Data[0].stats.station) plt.title(self.Data[0].stats.station)
plt.show() plt.show()
raw_input() raw_input()
plt.close(p) plt.close(p)
return return
islope = islope[0][0 :imax] islope = islope[0][0 :imax]
dataslope = self.Data[0].data[islope] dataslope = self.Data[0].data[islope]
# calculate slope as polynomal fit of order 1 # calculate slope as polynomal fit of order 1
xslope = np.arange(0, len(dataslope), 1) xslope = np.arange(0, len(dataslope), 1)
P = np.polyfit(xslope, dataslope, 1) P = np.polyfit(xslope, dataslope, 1)
datafit = np.polyval(P, xslope) datafit = np.polyval(P, xslope)
if datafit[0] >= datafit[len(datafit) - 1]: if datafit[0] >= datafit[len(datafit) - 1]:
print 'AICPicker: Negative slope, bad onset skipped!' print('AICPicker: Negative slope, bad onset skipped!')
return return
self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0]) self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
else: else:
self.SNR = None self.SNR = None
self.slope = None self.slope = None
if self.iplot > 1: if self.iplot > 1:
p = plt.figure(self.iplot) p = plt.figure(self.iplot)
x = self.Data[0].data x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k') p1, = plt.plot(self.Tcf, x / max(x), 'k')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r') p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
if self.Pick is not None: if self.Pick is not None:
p3, = plt.plot([self.Pick, self.Pick], [-0.1 , 0.5], 'b', linewidth=2) p3, = plt.plot([self.Pick, self.Pick], [-0.1 , 0.5], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick']) plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
else: else:
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF']) plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([]) plt.yticks([])
plt.title(self.Data[0].stats.station) plt.title(self.Data[0].stats.station)
if self.Pick is not None: if self.Pick is not None:
plt.figure(self.iplot + 1) plt.figure(self.iplot + 1)
p11, = plt.plot(self.Tcf, x, 'k') p11, = plt.plot(self.Tcf, x, 'k')
p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise]) p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r') p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
p14, = plt.plot(self.Tcf[islope], dataslope, 'g--') p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2) p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], \ plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'],
loc='best') loc='best')
plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \ plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
self.SNR, self.slope)) self.SNR, self.slope))
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.ylabel('Counts') plt.ylabel('Counts')
plt.yticks([]) plt.yticks([])
plt.show() plt.show()
raw_input() raw_input()
plt.close(p) plt.close(p)
if self.Pick == None: if self.Pick == None:
print 'AICPicker: Could not find minimum, picking window too short?' print('AICPicker: Could not find minimum, picking window too short?')
class PragPicker(AutoPicking): class PragPicker(AutoPicking):
@ -297,102 +297,102 @@ class PragPicker(AutoPicking):
def calcPick(self): def calcPick(self):
if self.getpick1() is not None: if self.getpick1() is not None:
print 'PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...' print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
self.Pick = None self.Pick = None
self.SNR = None self.SNR = None
self.slope = None self.slope = None
pickflag = 0 pickflag = 0
#smooth CF #smooth CF
ismooth = int(round(self.Tsmooth / self.dt)) ismooth = int(round(self.Tsmooth / self.dt))
cfsmooth = np.zeros(len(self.cf)) cfsmooth = np.zeros(len(self.cf))
if len(self.cf) < ismooth: if len(self.cf) < ismooth:
print 'PragPicker: Tsmooth larger than CF!' print('PragPicker: Tsmooth larger than CF!')
return return
else: else:
for i in range(1, len(self.cf)): for i in range(1, len(self.cf)):
if i > ismooth: if i > ismooth:
ii1 = i - ismooth; ii1 = i - ismooth;
cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
else: else:
cfsmooth[i] = np.mean(self.cf[1 : i]) cfsmooth[i] = np.mean(self.cf[1 : i])
#select picking window #select picking window
#which is centered around tpick1 #which is centered around tpick1
ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \ ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
& (self.Tcf <= self.getpick1() + self.PickWindow / 2)) & (self.Tcf <= self.getpick1() + self.PickWindow / 2))
cfipick = self.cf[ipick] - np.mean(self.cf[ipick]) cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
Tcfpick = self.Tcf[ipick] Tcfpick = self.Tcf[ipick]
cfsmoothipick = cfsmooth[ipick]- np.mean(self.cf[ipick]) cfsmoothipick = cfsmooth[ipick]- np.mean(self.cf[ipick])
ipick1 = np.argmin(abs(self.Tcf - self.getpick1())) ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
cfpick1 = 2 * self.cf[ipick1] cfpick1 = 2 * self.cf[ipick1]
#check trend of CF, i.e. differences of CF and adjust aus regarding this trend #check trend of CF, i.e. differences of CF and adjust aus regarding this trend
#prominent trend: decrease aus #prominent trend: decrease aus
#flat: use given aus #flat: use given aus
cfdiff = np.diff(cfipick); cfdiff = np.diff(cfipick)
i0diff = np.where(cfdiff > 0) i0diff = np.where(cfdiff > 0)
cfdiff = cfdiff[i0diff] cfdiff = cfdiff[i0diff]
minaus = min(cfdiff * (1 + self.aus)); minaus = min(cfdiff * (1 + self.aus))
aus1 = max([minaus, self.aus]); aus1 = max([minaus, self.aus])
#at first we look to the right until the end of the pick window is reached #at first we look to the right until the end of the pick window is reached
flagpick_r = 0 flagpick_r = 0
flagpick_l = 0 flagpick_l = 0
cfpick_r = 0 cfpick_r = 0
cfpick_l = 0 cfpick_l = 0
lpickwindow = int(round(self.PickWindow / self.dt)) lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])): for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]: if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]: if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]: if cfpick1 >= self.cf[i]:
pick_r = self.Tcf[i] pick_r = self.Tcf[i]
self.Pick = pick_r self.Pick = pick_r
flagpick_l = 1 flagpick_l = 1
cfpick_r = self.cf[i] cfpick_r = self.cf[i]
break break
# now we look to the left # now we look to the left
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1): for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]: if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]: if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]: if cfpick1 >= self.cf[i]:
pick_l = self.Tcf[i] pick_l = self.Tcf[i]
self.Pick = pick_l self.Pick = pick_l
flagpick_r = 1 flagpick_r = 1
cfpick_l = self.cf[i] cfpick_l = self.cf[i]
break break
# now decide which pick: left or right? # now decide which pick: left or right?
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r: if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
self.Pick = pick_l
pickflag = 1
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_r
pickflag = 1
elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_l self.Pick = pick_l
pickflag = 1 pickflag = 1
else: elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
print 'PragPicker: Could not find reliable onset!' self.Pick = pick_r
self.Pick = None pickflag = 1
pickflag = 0 elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_l
pickflag = 1
else:
print('PragPicker: Could not find reliable onset!')
self.Pick = None
pickflag = 0
if self.getiplot() > 1: if self.getiplot() > 1:
p = plt.figure(self.getiplot()) p = plt.figure(self.getiplot())
p1, = plt.plot(Tcfpick,cfipick, 'k') p1, = plt.plot(Tcfpick,cfipick, 'k')
p2, = plt.plot(Tcfpick,cfsmoothipick, 'r') p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
if pickflag > 0: if pickflag > 0:
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2) p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick']) plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([]) plt.yticks([])
plt.title(self.Data[0].stats.station) plt.title(self.Data[0].stats.station)
plt.show() plt.show()
raw_input() raw_input()
plt.close(p) plt.close(p)
else: else:
print 'PragPicker: No initial onset time given! Check input!' print('PragPicker: No initial onset time given! Check input!')
self.Pick = None self.Pick = None
return return