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New attribute getPickError in class EarlLatePicker to derive symmetric picking error out of earliest and latest possible and most probable pick.

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This commit is contained in:
Ludger Küperkoch 2015-03-09 16:14:03 +01:00
parent a3fb4770c6
commit 380cccdf16
1 changed files with 50 additions and 9 deletions
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59
pylot/core/pick/Picker.py
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@ -133,6 +133,9 @@ class AutoPicking(object):
def getEpick(self): def getEpick(self):
return self.EPick return self.EPick
def getPickError(self):
return self.PickError
def getiplot(self): def getiplot(self):
return self.iplot return self.iplot
@ -151,15 +154,18 @@ class AutoPicking(object):
class AICPicker(AutoPicking): class AICPicker(AutoPicking):
''' '''
Method to derive onset time of arriving phase based on CF Method to derive the onset time of an arriving phase based on CF
derived from AIC. derived from AIC. In order to get an impression of the quality of this inital pick,
a quality assessment is applied based on SNR and slope determination derived from the CF,
from which the AIC has been calculated.
''' '''
def calcPick(self): def calcPick(self):
print 'AICPicker: Get onset time (pick) from AIC-CF ...' print 'AICPicker: Get initial onset time (pick) from AIC-CF ...'
self.Pick = None self.Pick = None
self.PickError = 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:
@ -279,6 +285,7 @@ class PragPicker(AutoPicking):
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.PickError = None
self.SNR = None self.SNR = None
self.slope = None self.slope = None
#smooth CF #smooth CF
@ -377,6 +384,7 @@ class EarlLatePicker(AutoPicking):
self.LPick = None self.LPick = None
self.EPick = None self.EPick = None
self.PickError = None
self.SNR = None self.SNR = None
self.slope = None self.slope = None
if self.getpick1() is not None: if self.getpick1() is not None:
@ -509,6 +517,12 @@ class EarlLatePicker(AutoPicking):
#Ts/4 is assumed as time difference between most likely and earliest possible pick! #Ts/4 is assumed as time difference between most likely and earliest possible pick!
self.EPick = ti - Ts/4 self.EPick = ti - Ts/4
#get symmetric pick error as mean from earliest and latest possible pick
#by weighting latest possible pick tow times earliest possible pick
diffti_tl = self.LPick - ti
diffti_te = ti - self.EPick
self.PickError = (diffti_te + 2 * diffti_tl) / 3
if self.iplot is not None: if self.iplot is not None:
plt.figure(self.iplot) plt.figure(self.iplot)
if len(x) == 1: if len(x) == 1:
@ -517,14 +531,19 @@ class EarlLatePicker(AutoPicking):
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r') p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k') p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
p5, = plt.plot(zc, [0, 0, 0], '*g', markersize=14) p5, = plt.plot(zc, [0, 0, 0], '*g', markersize=14)
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings']) plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
loc='best')
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k') plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2) plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k') plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k') plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
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('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station) ax = plt.gca()
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
elif len(x) == 2: elif len(x) == 2:
plt.subplot(2,1,1) plt.subplot(2,1,1)
p1, = plt.plot(t, x[0].data, 'k') p1, = plt.plot(t, x[0].data, 'k')
@ -532,14 +551,19 @@ class EarlLatePicker(AutoPicking):
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r') p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k') p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14) p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings']) plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
loc='best')
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k') plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2) plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k') plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k') plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot(zc1[0:3], [0, 0, 0], '*g') plt.plot(zc1[0:3], [0, 0, 0], '*g')
plt.yticks([]) plt.yticks([])
plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station) ax = plt.gca()
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
plt.subplot(2,1,2) plt.subplot(2,1,2)
plt.plot(t, x[1].data, 'k') plt.plot(t, x[1].data, 'k')
plt.plot(t[inoise], x[1].data[inoise]) plt.plot(t[inoise], x[1].data[inoise])
@ -549,8 +573,12 @@ class EarlLatePicker(AutoPicking):
plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2) plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k') plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k') plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14) plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax = plt.gca()
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
plt.yticks([]) plt.yticks([])
elif len(x) == 3: elif len(x) == 3:
plt.subplot(3,1,1) plt.subplot(3,1,1)
@ -559,13 +587,18 @@ class EarlLatePicker(AutoPicking):
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r') p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k') p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14) p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings']) plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
loc='best')
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k') plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2) plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k') plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k') plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.yticks([]) plt.yticks([])
plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station) ax = plt.gca()
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % self.Data[0].stats.station)
plt.subplot(3,1,2) plt.subplot(3,1,2)
plt.plot(t, x[1].data, 'k') plt.plot(t, x[1].data, 'k')
plt.plot(t[inoise], x[1].data[inoise]) plt.plot(t[inoise], x[1].data[inoise])
@ -575,8 +608,12 @@ class EarlLatePicker(AutoPicking):
plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2) plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k') plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k') plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14) plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
plt.yticks([]) plt.yticks([])
ax = plt.gca()
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
plt.subplot(3,1,3) plt.subplot(3,1,3)
plt.plot(t, x[2].data, 'k') plt.plot(t, x[2].data, 'k')
plt.plot(t[inoise], x[2].data[inoise]) plt.plot(t[inoise], x[2].data[inoise])
@ -586,9 +623,13 @@ class EarlLatePicker(AutoPicking):
plt.plot([ti, ti], [max(x[2].data), -max(x[2].data)], 'b', linewidth=2) plt.plot([ti, ti], [max(x[2].data), -max(x[2].data)], 'b', linewidth=2)
plt.plot([self.LPick, self.LPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k') plt.plot([self.LPick, self.LPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
plt.plot([self.EPick, self.EPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k') plt.plot([self.EPick, self.EPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
plt.plot([ti + self.PickError, ti + self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot([ti - self.PickError, ti - self.PickError], [max(x[0].data)/2, -max(x[0].data)/2], 'r--')
plt.plot(zc3[0:3], [0, 0, 0], '*g', markersize=14) plt.plot(zc3[0:3], [0, 0, 0], '*g', markersize=14)
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([])
ax = plt.gca()
ax.set_xlim([self.Tcf[inoise[0][0]] - 2, self.Tcf[isignal[0][len(isignal) - 1]] + 3])
plt.show() plt.show()
raw_input() raw_input()
plt.close(self.iplot) plt.close(self.iplot)