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Debuged AIC picker for plotting smoothed CF instead of unsmoothed CF, implemented quick and dirty a temporary solution to process restituted data in order to calculate apropriate slope (line 204).

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This commit is contained in:
Ludger Küperkoch 2015-05-29 16:43:32 +02:00
parent 6e51c05c94
commit 5be662524f
1 changed files with 17 additions and 11 deletions
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26
pylot/core/pick/Picker.py
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@ -145,6 +145,8 @@ class AICPicker(AutoPicking):
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.SNR = None
#find NaN's #find NaN's
nn = np.isnan(self.cf) nn = np.isnan(self.cf)
if len(nn) > 1: if len(nn) > 1:
@ -197,11 +199,15 @@ class AICPicker(AutoPicking):
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!
#this is quick and dirty, better solution?
if max(self.Data[0].data < 1e-3):
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(self.cf[isignal] - np.mean(self.cf[isignal]))) / max(abs(self.cf[inoise] \ self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / max(abs(aic[inoise] \
- np.mean(self.cf[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
@ -230,8 +236,8 @@ class AICPicker(AutoPicking):
self.SNR = None self.SNR = None
self.slope = None self.slope = None
if self.iplot is not None: if self.iplot > 1:
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')
@ -243,7 +249,6 @@ class AICPicker(AutoPicking):
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()
if self.Pick is not None: if self.Pick is not None:
plt.figure(self.iplot + 1) plt.figure(self.iplot + 1)
@ -259,11 +264,12 @@ class AICPicker(AutoPicking):
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')
ax = plt.gca() ax = plt.gca()
ax.set_ylim([-10, max(self.Data[0].data)]) plt.yticks([])
ax.set_xlim([self.Tcf[inoise[0][0]] - 5, self.Tcf[isignal[0][len(isignal) - 1]] + 5]) ax.set_xlim([self.Tcf[inoise[0][0]] - 5, self.Tcf[isignal[0][len(isignal) - 1]] + 5])
plt.show()
raw_input() raw_input()
plt.close(self.iplot) 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?'
@ -347,8 +353,8 @@ class PragPicker(AutoPicking):
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r: elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_r self.Pick = pick_r
if self.getiplot() is not None: if self.getiplot() > 1:
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')
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)
@ -358,7 +364,7 @@ class PragPicker(AutoPicking):
plt.title(self.Data[0].stats.station) plt.title(self.Data[0].stats.station)
plt.show() plt.show()
raw_input() raw_input()
plt.close(self.getiplot()) plt.close(p)
else: else:
self.Pick = None self.Pick = None