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Changed index for AR-CF calculation, no more shift in getTimeArray needed.

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Ludger Küperkoch 2015-02-24 09:08:38 +01:00
parent acd8f70369
commit 3556a2becc
1 changed files with 8 additions and 12 deletions
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20
pylot/core/pick/CharFuns.py
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@ -18,6 +18,7 @@ autoregressive prediction: application ot local and regional distances, Geophys.
import numpy as np import numpy as np
from obspy.core import Stream from obspy.core import Stream
import pdb import pdb
import matplotlib.pyplot as plt
class CharacteristicFunction(object): class CharacteristicFunction(object):
''' '''
@ -117,13 +118,8 @@ class CharacteristicFunction(object):
return self.dt return self.dt
def getTimeArray(self): def getTimeArray(self):
if self.getTime1():
shift = self.getTime2()
else:
shift = 0
incr = self.getIncrement() incr = self.getIncrement()
self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0] \ self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0]
+ shift
return self.TimeArray return self.TimeArray
def getFnoise(self): def getFnoise(self):
@ -316,7 +312,7 @@ class ARZcf(CharacteristicFunction):
cf = np.zeros(len(xnp)) cf = np.zeros(len(xnp))
loopstep = self.getARdetStep() loopstep = self.getARdetStep()
arcalci = ldet + self.getOrder() - 1 #AR-calculation index arcalci = ldet + self.getOrder() - 1 #AR-calculation index
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1): for i in range(ldet + self.getOrder() - 1, tend - 2 * lpred + 1):
if i == arcalci: if i == arcalci:
#determination of AR coefficients #determination of AR coefficients
#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]! #to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
@ -325,7 +321,7 @@ class ARZcf(CharacteristicFunction):
#AR prediction of waveform using calculated AR coefficients #AR prediction of waveform using calculated AR coefficients
self.arPredZ(xnp, self.arpara, i + 1, lpred) self.arPredZ(xnp, self.arpara, i + 1, lpred)
#prediction error = CF #prediction error = CF
cf[i] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred] - xnp[i:i + lpred], 2)) / lpred) cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred] - xnp[i:i + lpred], 2)) / lpred)
nn = np.isnan(cf) nn = np.isnan(cf)
if len(nn) > 1: if len(nn) > 1:
cf[nn] = 0 cf[nn] = 0
@ -438,7 +434,7 @@ class ARHcf(CharacteristicFunction):
cf = np.zeros(tend - lpred + 1) cf = np.zeros(tend - lpred + 1)
loopstep = self.getARdetStep() loopstep = self.getARdetStep()
arcalci = ldet + self.getOrder() - 1 #AR-calculation index arcalci = ldet + self.getOrder() - 1 #AR-calculation index
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1): for i in range(ldet + self.getOrder() - 1, tend - 2 * lpred + 1):
if i == arcalci: if i == arcalci:
#determination of AR coefficients #determination of AR coefficients
#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]! #to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
@ -447,7 +443,7 @@ class ARHcf(CharacteristicFunction):
#AR prediction of waveform using calculated AR coefficients #AR prediction of waveform using calculated AR coefficients
self.arPredH(xnp, self.arpara, i + 1, lpred) self.arPredH(xnp, self.arpara, i + 1, lpred)
#prediction error = CF #prediction error = CF
cf[i] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \ cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2)) / (2 * lpred)) + np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2)) / (2 * lpred))
nn = np.isnan(cf) nn = np.isnan(cf)
if len(nn) > 1: if len(nn) > 1:
@ -570,7 +566,7 @@ class AR3Ccf(CharacteristicFunction):
cf = np.zeros(tend - lpred + 1) cf = np.zeros(tend - lpred + 1)
loopstep = self.getARdetStep() loopstep = self.getARdetStep()
arcalci = ldet + self.getOrder() - 1 #AR-calculation index arcalci = ldet + self.getOrder() - 1 #AR-calculation index
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1): for i in range(ldet + self.getOrder() - 1, tend - 2 * lpred + 1):
if i == arcalci: if i == arcalci:
#determination of AR coefficients #determination of AR coefficients
#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]! #to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
@ -580,7 +576,7 @@ class AR3Ccf(CharacteristicFunction):
#AR prediction of waveform using calculated AR coefficients #AR prediction of waveform using calculated AR coefficients
self.arPred3C(xnp, self.arpara, i + 1, lpred) self.arPred3C(xnp, self.arpara, i + 1, lpred)
#prediction error = CF #prediction error = CF
cf[i] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \ cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2) \ + np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2) \
+ np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / (3 * lpred)) + np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / (3 * lpred))
nn = np.isnan(cf) nn = np.isnan(cf)