AR-CFs now have same sampling rate as raw seismograms, new attribute getXCF
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Ludger Küperkoch
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16c07da6e4
commit
acd8f70369
@ -17,12 +17,13 @@ autoregressive prediction: application ot local and regional distances, Geophys.
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"""
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import numpy as np
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from obspy.core import Stream
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import pdb
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class CharacteristicFunction(object):
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'''
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SuperClass for different types of characteristic functions.
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'''
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def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=0.001):
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def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None):
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'''
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Initialize data type object with information from the original
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Seismogram.
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@ -117,12 +118,12 @@ class CharacteristicFunction(object):
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def getTimeArray(self):
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if self.getTime1():
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incr = self.getARdetStep()[0]
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self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0] \
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+ self.getTime1() + self.getTime2()
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shift = self.getTime2()
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else:
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incr = self.getIncrement()
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self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0]
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shift = 0
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incr = self.getIncrement()
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self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0] \
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+ shift
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return self.TimeArray
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def getFnoise(self):
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@ -134,6 +135,9 @@ class CharacteristicFunction(object):
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def getCF(self):
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return self.cf
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def getXCF(self):
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return self.xcf
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def getDataArray(self, cut=None):
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'''
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If cut times are given, time series is cut from cut[0] (start time)
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@ -226,9 +230,8 @@ class AICcf(CharacteristicFunction):
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cumsumcf = np.cumsum(np.power(xnp, 2))
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i = np.where(cumsumcf == 0)
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cumsumcf[i] = np.finfo(np.float64).eps
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cf[k] = ((k - 1) * np.log(cumsumcf[k] / k) + (datlen - k + 1) *
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np.log((cumsumcf[datlen - 1] -
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cumsumcf[k - 1]) / (datlen - k + 1)))
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cf[k] = ((k - 1) * np.log(cumsumcf[k] / k) + (datlen - k + 1) * \
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np.log((cumsumcf[datlen - 1] - cumsumcf[k - 1]) / (datlen - k + 1)))
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cf[0] = cf[1]
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inf = np.isinf(cf)
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ff = np.where(inf == True)
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@ -236,6 +239,7 @@ class AICcf(CharacteristicFunction):
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cf[ff] = 0
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self.cf = cf - np.mean(cf)
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self.xcf = xnp
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class HOScf(CharacteristicFunction):
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@ -287,6 +291,7 @@ class HOScf(CharacteristicFunction):
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if len(nn) > 1:
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LTA[nn] = 0
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self.cf = LTA
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self.xcf = xnp
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class ARZcf(CharacteristicFunction):
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@ -308,24 +313,24 @@ class ARZcf(CharacteristicFunction):
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ldet = int(round(self.getTime1() / self.getIncrement())) #length of AR-determination window [samples]
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lpred = int(np.ceil(self.getTime2() / self.getIncrement())) #length of AR-prediction window [samples]
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cf = []
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cf = np.zeros(len(xnp))
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loopstep = self.getARdetStep()
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
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#determination of AR coefficients
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self.arDetZ(xnoise, self.getOrder(), i-ldet, i)
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arcalci = ldet + self.getOrder() - 1 #AR-calculation index
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1):
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if i == arcalci:
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#determination of AR coefficients
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#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
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self.arDetZ(xnoise, self.getOrder(), i-ldet, i)
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arcalci = arcalci + loopstep[1]
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#AR prediction of waveform using calculated AR coefficients
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self.arPredZ(xnp, self.arpara, i + 1, lpred)
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#prediction error = CF
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err = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred] - xnp[i:i + lpred], 2)) / lpred)
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cf.append(err)
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#convert list to numpy array
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cf = np.asarray(cf)
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cf[i] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred] - xnp[i:i + lpred], 2)) / lpred)
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nn = np.isnan(cf)
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if len(nn) > 1:
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cf[nn] = 0
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self.cf = cf
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self.xcf = xnp
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def arDetZ(self, data, order, rind, ldet):
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'''
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@ -430,23 +435,25 @@ class ARHcf(CharacteristicFunction):
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ldet = int(round(self.getTime1() / self.getIncrement())) #length of AR-determination window [samples]
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lpred = int(np.ceil(self.getTime2() / self.getIncrement())) #length of AR-prediction window [samples]
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cf = []
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cf = np.zeros(tend - lpred + 1)
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loopstep = self.getARdetStep()
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
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self.arDetH(Xnoise, self.getOrder(), i-ldet, i)
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arcalci = ldet + self.getOrder() - 1 #AR-calculation index
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1):
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if i == arcalci:
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#determination of AR coefficients
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#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
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self.arDetH(Xnoise, self.getOrder(), i-ldet, i)
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arcalci = arcalci + loopstep[1]
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#AR prediction of waveform using calculated AR coefficients
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self.arPredH(xnp, self.arpara, i + 1, lpred)
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#prediction error = CF
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err = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
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cf[i] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
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+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2)) / (2 * lpred))
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cf.append(err)
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#convert list to numpy array
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cf = np.asarray(cf)
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nn = np.isnan(cf)
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if len(nn) > 1:
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cf[nn] = 0
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self.cf = cf
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self.xcf = xnp
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def arDetH(self, data, order, rind, ldet):
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'''
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@ -560,24 +567,27 @@ class AR3Ccf(CharacteristicFunction):
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ldet = int(round(self.getTime1() / self.getIncrement())) #length of AR-determination window [samples]
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lpred = int(np.ceil(self.getTime2() / self.getIncrement())) #length of AR-prediction window [samples]
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cf = []
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cf = np.zeros(tend - lpred + 1)
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loopstep = self.getARdetStep()
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
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self.arDet3C(Xnoise, self.getOrder(), i-ldet, i)
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arcalci = ldet + self.getOrder() - 1 #AR-calculation index
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1):
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if i == arcalci:
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#determination of AR coefficients
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#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
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self.arDet3C(Xnoise, self.getOrder(), i-ldet, i)
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arcalci = arcalci + loopstep[1]
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#AR prediction of waveform using calculated AR coefficients
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self.arPred3C(xnp, self.arpara, i + 1, lpred)
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#prediction error = CF
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err = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
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cf[i] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
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+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2) \
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+ np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / (3 * lpred))
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cf.append(err)
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#convert list to numpy array
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cf = np.asarray(cf)
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nn = np.isnan(cf)
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if len(nn) > 1:
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cf[nn] = 0
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self.cf = cf
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self.xcf = xnp
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def arDet3C(self, data, order, rind, ldet):
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'''
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