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Debuged, stable slope determination of CF, modified plotting.

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This commit is contained in:
Ludger Küperkoch 2015-03-04 15:52:14 +01:00
parent 714e70de69
commit 8f71297884
1 changed files with 16 additions and 4 deletions
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20
pylot/core/pick/Picker.py
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@ -213,11 +213,19 @@ class AICPicker(AutoPicking):
#get slope window #get slope 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
#'cause slope should be calculated up to first local minimum only!
imax = np.argmax(self.Data[0].data[islope])
islope = islope[0][0 :imax]
dataslope = self.Data[0].data[islope] dataslope = self.Data[0].data[islope]
#calculate slope as linear regression 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]:
print 'AICPicker: Negative slope, bad onset skipped!'
return
self.slope = 1 / tslope * datafit[len(dataslope) - 1] - datafit[0] self.slope = 1 / tslope * datafit[len(dataslope) - 1] - datafit[0]
else: else:
@ -241,13 +249,17 @@ class AICPicker(AutoPicking):
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')
plt.title('SNR and Slope, Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \ plt.title('SNR and Slope, 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')
ax = plt.gca()
ax.set_ylim([-10, max(self.Data[0].data)])
ax.set_xlim([self.Tcf[inoise[0][0]] - 5, self.Tcf[isignal[0][len(isignal) - 1]] + 5])
raw_input() raw_input()
plt.close(self.iplot) plt.close(self.iplot)