From acd8f70369a8d8d031ee0ba338e9ee49b5db4f36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ludger=20K=C3=BCperkoch?= Date: Mon, 23 Feb 2015 15:42:35 +0100 Subject: [PATCH] AR-CFs now have same sampling rate as raw seismograms, new attribute getXCF --- pylot/core/pick/CharFuns.py | 80 +++++++++++++++++++++---------------- 1 file changed, 45 insertions(+), 35 deletions(-) diff --git a/pylot/core/pick/CharFuns.py b/pylot/core/pick/CharFuns.py index d52b7898..9332f828 100644 --- a/pylot/core/pick/CharFuns.py +++ b/pylot/core/pick/CharFuns.py @@ -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): '''