AR-CFs now have same sampling rate as raw seismograms, new attribute getXCF

This commit is contained in:
Ludger Küperkoch 2015-02-23 15:42:35 +01:00
parent 16c07da6e4
commit acd8f70369

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@ -17,12 +17,13 @@ autoregressive prediction: application ot local and regional distances, Geophys.
"""
import numpy as np
from obspy.core import Stream
import pdb
class CharacteristicFunction(object):
'''
SuperClass for different types of characteristic functions.
'''
def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=0.001):
def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None):
'''
Initialize data type object with information from the original
Seismogram.
@ -117,12 +118,12 @@ class CharacteristicFunction(object):
def getTimeArray(self):
if self.getTime1():
incr = self.getARdetStep()[0]
self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0] \
+ self.getTime1() + self.getTime2()
shift = self.getTime2()
else:
incr = self.getIncrement()
self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0]
shift = 0
incr = self.getIncrement()
self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0] \
+ shift
return self.TimeArray
def getFnoise(self):
@ -134,6 +135,9 @@ class CharacteristicFunction(object):
def getCF(self):
return self.cf
def getXCF(self):
return self.xcf
def getDataArray(self, cut=None):
'''
If cut times are given, time series is cut from cut[0] (start time)
@ -226,9 +230,8 @@ class AICcf(CharacteristicFunction):
cumsumcf = np.cumsum(np.power(xnp, 2))
i = np.where(cumsumcf == 0)
cumsumcf[i] = np.finfo(np.float64).eps
cf[k] = ((k - 1) * np.log(cumsumcf[k] / k) + (datlen - k + 1) *
np.log((cumsumcf[datlen - 1] -
cumsumcf[k - 1]) / (datlen - k + 1)))
cf[k] = ((k - 1) * np.log(cumsumcf[k] / k) + (datlen - k + 1) * \
np.log((cumsumcf[datlen - 1] - cumsumcf[k - 1]) / (datlen - k + 1)))
cf[0] = cf[1]
inf = np.isinf(cf)
ff = np.where(inf == True)
@ -236,6 +239,7 @@ class AICcf(CharacteristicFunction):
cf[ff] = 0
self.cf = cf - np.mean(cf)
self.xcf = xnp
class HOScf(CharacteristicFunction):
@ -287,6 +291,7 @@ class HOScf(CharacteristicFunction):
if len(nn) > 1:
LTA[nn] = 0
self.cf = LTA
self.xcf = xnp
class ARZcf(CharacteristicFunction):
@ -308,24 +313,24 @@ class ARZcf(CharacteristicFunction):
ldet = int(round(self.getTime1() / self.getIncrement())) #length of AR-determination window [samples]
lpred = int(np.ceil(self.getTime2() / self.getIncrement())) #length of AR-prediction window [samples]
cf = []
cf = np.zeros(len(xnp))
loopstep = self.getARdetStep()
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
#determination of AR coefficients
self.arDetZ(xnoise, self.getOrder(), i-ldet, i)
arcalci = ldet + self.getOrder() - 1 #AR-calculation index
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1):
if i == arcalci:
#determination of AR coefficients
#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
self.arDetZ(xnoise, self.getOrder(), i-ldet, i)
arcalci = arcalci + loopstep[1]
#AR prediction of waveform using calculated AR coefficients
self.arPredZ(xnp, self.arpara, i + 1, lpred)
#prediction error = CF
err = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred] - xnp[i:i + lpred], 2)) / lpred)
cf.append(err)
#convert list to numpy array
cf = np.asarray(cf)
cf[i] = 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
self.cf = cf
self.xcf = xnp
def arDetZ(self, data, order, rind, ldet):
'''
@ -430,23 +435,25 @@ class ARHcf(CharacteristicFunction):
ldet = int(round(self.getTime1() / self.getIncrement())) #length of AR-determination window [samples]
lpred = int(np.ceil(self.getTime2() / self.getIncrement())) #length of AR-prediction window [samples]
cf = []
cf = np.zeros(tend - lpred + 1)
loopstep = self.getARdetStep()
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
self.arDetH(Xnoise, self.getOrder(), i-ldet, i)
arcalci = ldet + self.getOrder() - 1 #AR-calculation index
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1):
if i == arcalci:
#determination of AR coefficients
#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
self.arDetH(Xnoise, self.getOrder(), i-ldet, i)
arcalci = arcalci + loopstep[1]
#AR prediction of waveform using calculated AR coefficients
self.arPredH(xnp, self.arpara, i + 1, lpred)
#prediction error = CF
err = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
cf[i] = 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))
cf.append(err)
#convert list to numpy array
cf = np.asarray(cf)
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
self.cf = cf
self.xcf = xnp
def arDetH(self, data, order, rind, ldet):
'''
@ -560,24 +567,27 @@ class AR3Ccf(CharacteristicFunction):
ldet = int(round(self.getTime1() / self.getIncrement())) #length of AR-determination window [samples]
lpred = int(np.ceil(self.getTime2() / self.getIncrement())) #length of AR-prediction window [samples]
cf = []
cf = np.zeros(tend - lpred + 1)
loopstep = self.getARdetStep()
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
self.arDet3C(Xnoise, self.getOrder(), i-ldet, i)
arcalci = ldet + self.getOrder() - 1 #AR-calculation index
for i in range(ldet + self.getOrder() - 1, tend - lpred + 1):
if i == arcalci:
#determination of AR coefficients
#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
self.arDet3C(Xnoise, self.getOrder(), i-ldet, i)
arcalci = arcalci + loopstep[1]
#AR prediction of waveform using calculated AR coefficients
self.arPred3C(xnp, self.arpara, i + 1, lpred)
#prediction error = CF
err = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
cf[i] = 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))
cf.append(err)
#convert list to numpy array
cf = np.asarray(cf)
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
self.cf = cf
self.xcf = xnp
def arDet3C(self, data, order, rind, ldet):
'''