Implemented quality assessment for AICPicker based on slope and SNR from CF. New attributes getSNR and getSlope.
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Ludger Küperkoch
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cc2d823272
commit
f6922fafef
@ -33,7 +33,7 @@ class AutoPicking(object):
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:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
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:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
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in EarlLatePick
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in EarlLatePicker
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:type: int
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:param: TSNR, length of time windows around pick used to determine SNR [s]
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@ -121,6 +121,12 @@ class AutoPicking(object):
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def getpick(self):
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return self.Pick
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def getSNR(self):
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return self.SNR
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def getSlope(self):
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return self.slope
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def getLpick(self):
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return self.LPick
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@ -151,7 +157,7 @@ class AICPicker(AutoPicking):
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def calcPick(self):
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print 'Get onset time (pick) from AIC-CF ...'
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print 'AICPicker: Get onset time (pick) from AIC-CF ...'
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self.Pick = None
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#find NaN's
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@ -187,6 +193,37 @@ class AICPicker(AutoPicking):
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self.Pick = self.Tcf[i]
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break
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#quality assessment using SNR and slope from CF
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if self.Pick is not None:
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#some parameters needed:
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tnoise = self.TSNR[0] #noise window length for calculating noise level
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tsignal = self.TSNR[2] #signal window length
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tsafety = self.TSNR[1] #safety gap between signal onset and noise window
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tslope = self.TSNR[3] #slope determination window
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#get noise window
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inoise = np.where((self.Tcf <= max([self.Pick - tsafety, 0])) \
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& (self.Tcf >= max([self.Pick - tnoise - tsafety, 0])))
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#get signal window
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isignal = np.where((self.Tcf <= min([self.Pick + tsignal + tsafety, len(self.Data[0].data)])) \
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& (self.Tcf >= self.Pick))
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#calculate SNR from CF
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self.SNR = max(abs(self.cf[isignal] - np.mean(self.cf[isignal]))) / max(abs(self.cf[inoise] \
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- np.mean(self.cf[inoise])))
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#calculate slope from CF after initial pick
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#get slope window
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islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
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& (self.Tcf >= self.Pick))
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dataslope = self.Data[0].data[islope]
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#calculate slope as linear regression of order 1
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xslope = np.arange(0, len(dataslope), 1)
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P = np.polyfit(xslope, dataslope, 1)
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datafit = np.polyval(P, xslope)
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self.slope = 1 / tslope * datafit[len(dataslope) - 1] - datafit[0]
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else:
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self.SNR = None
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self.slope = None
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if self.iplot is not None:
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plt.figure(self.iplot)
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x = self.Data[0].data
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@ -198,14 +235,26 @@ class AICPicker(AutoPicking):
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plt.yticks([])
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plt.title(self.Data[0].stats.station)
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plt.show()
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if self.Pick is not None:
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plt.figure(self.iplot + 1)
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p11, = plt.plot(self.Tcf, x, 'k')
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p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
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p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
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p14, = plt.plot(self.Tcf[islope], dataslope, 'g')
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p15, = plt.plot(self.Tcf[islope], datafit, 'g--', linewidth=2)
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plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'])
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plt.title('SNR and Slope, Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \
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self.SNR, self.slope))
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.ylabel('Counts')
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raw_input()
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plt.close(self.iplot)
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if self.Pick == None:
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print 'AICPicker: Could not find minimum, picking window too short?'
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return self.Pick
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class PragPicker(AutoPicking):
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'''
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@ -215,9 +264,11 @@ class PragPicker(AutoPicking):
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def calcPick(self):
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if self.getpick1() is not None:
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print 'Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
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print 'PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
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self.Pick = None
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self.SNR = None
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self.slope = None
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#smooth CF
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ismooth = int(round(self.Tsmooth / self.dt))
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cfsmooth = np.zeros(len(self.cf))
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@ -314,22 +365,26 @@ class EarlLatePicker(AutoPicking):
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self.LPick = None
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self.EPick = None
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self.SNR = None
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self.slope = None
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if self.getpick1() is not None:
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print 'Get earliest and latest possible pick relative to most likely pick ...'
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print 'EarlLatePicker: Get earliest and latest possible pick relative to most likely pick ...'
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ti = self.getpick1()
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x = self.Data
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t = self.Tcf
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#some parmaters needed:
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#some parameters needed:
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tnoise = self.TSNR[0] #noise window length for calculating noise level
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tsignal = self.TSNR[2] #signal window length
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tsafety = self.TSNR[1] #safety gap between signal onset and noise window
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#get latest possible pick
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#get noise window
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inoise = np.where((self.Tcf <= ti - tsafety) & (self.Tcf >= ti - tnoise - tsafety))
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inoise = np.where((self.Tcf <= max([ti - tsafety, 0])) \
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& (self.Tcf >= max([ti - tnoise - tsafety, 0])))
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#get signal window
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isignal = np.where((self.Tcf <= ti + tsignal) & (self.Tcf >= ti))
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isignal = np.where((self.Tcf <= min([ti + tsignal + tsafety, len(x[0].data)])) \
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& (self.Tcf >= ti))
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#calculate noise level
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if len(x) == 1:
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nlevel = max(abs(x[0].data[inoise])) * self.nfac
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@ -337,7 +392,7 @@ class EarlLatePicker(AutoPicking):
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ilup = np.where(x[0].data[isignal] > nlevel)
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ildown = np.where(x[0].data[isignal] < -nlevel)
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if len(ilup[0]) <= 1 and len(ildown[0]) <= 1:
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print 'EarlLatePick: Signal lower than noise level, misspick?'
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print 'EarlLatePicker: Signal lower than noise level, misspick?'
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return
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il = min([ilup[0][0], ildown[0][0]])
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self.LPick = t[isignal][il]
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@ -351,7 +406,7 @@ class EarlLatePicker(AutoPicking):
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ilup = min([ilup1[0][0], ilup2[0][0]])
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ildown = min([ildown1[0][0], ildown2[0][0]])
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if np.size(ilup) < 1 and np.size(ildown) < 1:
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print 'EarlLatePick: Signal lower than noise level, misspick?'
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print 'EarlLatePicker: Signal lower than noise level, misspick?'
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return
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il = min([ilup, ildown])
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self.LPick = t[isignal][il]
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@ -368,7 +423,7 @@ class EarlLatePicker(AutoPicking):
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ilup = min([ilup1[0][0], ilup2[0][0], ilup3[0][0]])
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ildown = min([ildown1[0][0], ildown2[0][0], ildown3[0][0]])
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if np.size(ilup) < 1 and np.size(ildown) < 1:
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print 'EarlLatePick: Signal lower than noise level, misspick?'
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print 'EarlLatePicker: Signal lower than noise level, misspick?'
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return
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il = min([ilup, ildown])
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self.LPick = t[isignal][il]
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@ -527,6 +582,6 @@ class EarlLatePicker(AutoPicking):
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plt.close(self.iplot)
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elif self.getpick1() == None:
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print 'EarlLatePick: No initial onset time given! Check input!'
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print 'EarlLatePicker: No initial onset time given! Check input!'
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return
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