Running indicies changed after kai Olbert to calculate equal CF as done in MatLab, implemented some tools to compensate numerical artefacts.
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Ludger Küperkoch
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1c71286fd8
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6e51c05c94
@ -218,13 +218,12 @@ class AICcf(CharacteristicFunction):
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nn = np.isnan(xnp)
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if len(nn) > 1:
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xnp[nn] = 0
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i0 = np.where(xnp == 0)
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i = np.where(xnp > 0)
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xnp[i0] = xnp[i[0][0]]
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datlen = len(xnp)
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k = np.arange(1, datlen)
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cf = np.zeros(datlen)
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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] - cumsumcf[k - 1]) / (datlen - k + 1)))
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cf[0] = cf[1]
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@ -236,7 +235,6 @@ class AICcf(CharacteristicFunction):
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self.cf = cf - np.mean(cf)
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self.xcf = x
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class HOScf(CharacteristicFunction):
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'''
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Function to calculate skewness (statistics of order 3) or kurtosis
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@ -310,8 +308,8 @@ class ARZcf(CharacteristicFunction):
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cf = np.zeros(len(xnp))
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loopstep = self.getARdetStep()
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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 - 2 * lpred + 1):
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arcalci = ldet + self.getOrder() #AR-calculation index
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for i in range(ldet + self.getOrder(), 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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@ -320,10 +318,17 @@ class ARZcf(CharacteristicFunction):
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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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cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred] - xnp[i:i + lpred], 2)) / lpred)
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cf[i + lpred-1] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred-1] - xnp[i:i + lpred-1], 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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#remove zeros and artefacts
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tap = np.hanning(len(cf))
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cf = tap * cf
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io = np.where(cf == 0)
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ino = np.where(cf > 0)
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cf[io] = cf[ino[0][0]]
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self.cf = cf
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self.xcf = x
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@ -350,17 +355,18 @@ class ARZcf(CharacteristicFunction):
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#recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
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rhs = np.zeros(self.getOrder())
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for k in range(0, self.getOrder()):
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for i in range(rind, ldet):
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rhs[k] = rhs[k] + data[i] * data[i - k]
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for i in range(rind, ldet+1):
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ki = k + 1
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rhs[k] = rhs[k] + data[i] * data[i - ki]
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#recursive calculation of data array (second sum at left part of eq. 6.5 in Kueperkoch et al. 2012)
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A = np.zeros((2,2))
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A = np.zeros((self.getOrder(),self.getOrder()))
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for k in range(1, self.getOrder() + 1):
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for j in range(1, k + 1):
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for i in range(rind, ldet):
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for i in range(rind, ldet+1):
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ki = k - 1
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ji = j - 1
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A[ki,ji] = A[ki,ji] + data[i - ji] * data[i - ki]
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A[ki,ji] = A[ki,ji] + data[i - j] * data[i - k]
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A[ji,ki] = A[ki,ji]
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@ -387,20 +393,20 @@ class ARZcf(CharacteristicFunction):
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Output: predicted waveform z
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'''
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#be sure of the summation indeces
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if rind < len(arpara) + 1:
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rind = len(arpara) + 1
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if rind > len(data) - lpred + 1:
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rind = len(data) - lpred + 1
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if rind < len(arpara):
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rind = len(arpara)
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if rind > len(data) - lpred :
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rind = len(data) - lpred
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if lpred < 1:
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lpred = 1
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if lpred > len(data) - 1:
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lpred = len(data) - 1
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if lpred > len(data) - 2:
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lpred = len(data) - 2
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z = np.append(data[0:rind], np.zeros(lpred))
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for i in range(rind, rind + lpred):
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for j in range(1, len(arpara) + 1):
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ji = j - 1
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z[i] = z[i] + arpara[ji] * z[i - ji]
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z[i] = z[i] + arpara[ji] * z[i - j]
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self.xpred = z
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@ -432,8 +438,9 @@ class ARHcf(CharacteristicFunction):
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cf = np.zeros(len(xenoise))
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loopstep = self.getARdetStep()
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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 - 2 * lpred + 1):
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arcalci = lpred + self.getOrder() - 1 #AR-calculation index
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#arcalci = ldet + self.getOrder() - 1 #AR-calculation index
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for i in range(lpred + self.getOrder() - 1, tend - 2 * 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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@ -447,6 +454,13 @@ class ARHcf(CharacteristicFunction):
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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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#remove zeros and artefacts
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tap = np.hanning(len(cf))
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cf = tap * cf
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io = np.where(cf == 0)
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ino = np.where(cf > 0)
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cf[io] = cf[ino[0][0]]
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self.cf = cf
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self.xcf = xnp
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@ -581,6 +595,13 @@ class AR3Ccf(CharacteristicFunction):
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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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#remove zeros and artefacts
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tap = np.hanning(len(cf))
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cf = tap * cf
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io = np.where(cf == 0)
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ino = np.where(cf > 0)
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cf[io] = cf[ino[0][0]]
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self.cf = cf
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self.xcf = xnp
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