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

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This commit is contained in:
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
from obspy.core import Stream
import pdb
import matplotlib.pyplot as plt
class CharacteristicFunction(object):
'''
@ -117,13 +118,8 @@ class CharacteristicFunction(object):
return self.dt
def getTimeArray(self):
if self.getTime1():
shift = self.getTime2()
else:
shift = 0
incr = self.getIncrement()
self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0] \
+ shift
self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0]
return self.TimeArray
def getFnoise(self):
@ -316,7 +312,7 @@ class ARZcf(CharacteristicFunction):
cf = np.zeros(len(xnp))
loopstep = self.getARdetStep()
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:
#determination of AR coefficients
#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
self.arPredZ(xnp, self.arpara, i + 1, lpred)
#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)
if len(nn) > 1:
cf[nn] = 0
@ -438,7 +434,7 @@ class ARHcf(CharacteristicFunction):
cf = np.zeros(tend - lpred + 1)
loopstep = self.getARdetStep()
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:
#determination of AR coefficients
#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
self.arPredH(xnp, self.arpara, i + 1, lpred)
#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))
nn = np.isnan(cf)
if len(nn) > 1:
@ -570,7 +566,7 @@ class AR3Ccf(CharacteristicFunction):
cf = np.zeros(tend - lpred + 1)
loopstep = self.getARdetStep()
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:
#determination of AR coefficients
#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
self.arPred3C(xnp, self.arpara, i + 1, lpred)
#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[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / (3 * lpred))
nn = np.isnan(cf)