Debugged getDataArray, same data lengths are now guaranteed
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Ludger Küperkoch
parent
13b8a9daec
commit
2fcf325a6e
@ -117,8 +117,8 @@ class CharacteristicFunction(object):
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def getTimeArray(self):
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if self.getTime1():
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incr = self.getARdetStep()
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self.TimeArray = np.arange(0, len(self.getCF()) * incr[0], incr[0]) + self.getCut()[0] \
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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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else:
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incr = self.getIncrement()
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@ -143,18 +143,31 @@ class CharacteristicFunction(object):
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cutting window
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'''
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if cut is not None:
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if self.cut[0] == 0:
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start = 0
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else:
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start = self.cut[0] / self.dt
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stop = self.cut[1] / self.dt
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if len(self.orig_data) == 1:
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if self.cut[0] == 0 and self.cut[1] == 0:
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start = 0
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stop = len(self.orig_data[0])
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elif self.cut[0] == 0 and self.cut[1] is not 0:
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start = 0
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stop = self.cut[1] / self.dt
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else:
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start = self.cut[0] / self.dt
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stop = self.cut[1] / self.dt
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zz = self.orig_data.copy()
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z1 = zz[0].copy()
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zz[0].data = z1.data[start:stop]
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data = zz
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return data
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elif len(self.orig_data) == 2:
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if self.cut[0] == 0 and self.cut[1] == 0:
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start = 0
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stop = min([len(self.orig_data[0]), len(self.orig_data[1])])
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elif self.cut[0] == 0 and self.cut[1] is not 0:
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start = 0
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stop = self.cut[1] / self.dt
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else:
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start = self.cut[0] / self.dt
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stop = self.cut[1] / self.dt
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hh = self.orig_data.copy()
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h1 = hh[0].copy()
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h2 = hh[1].copy()
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@ -163,6 +176,15 @@ class CharacteristicFunction(object):
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data = hh
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return data
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elif len(self.orig_data) == 3:
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if self.cut[0] == 0 and self.cut[1] == 0:
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start = 0
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stop = min([len(self.orig_data[0]), len(self.orig_data[1]), len(self.orig_data[2])])
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elif self.cut[0] == 0 and self.cut[1] is not 0:
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start = 0
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stop = self.cut[1] / self.dt
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else:
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start = self.cut[0] / self.dt
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stop = self.cut[1] / self.dt
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hh = self.orig_data.copy()
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h1 = hh[0].copy()
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h2 = hh[1].copy()
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@ -195,6 +217,9 @@ class AICcf(CharacteristicFunction):
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print 'Calculating AIC ...'
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x = self.getDataArray()
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xnp = x[0].data
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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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datlen = len(xnp)
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k = np.arange(1, datlen)
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cf = np.zeros(datlen)
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@ -224,6 +249,9 @@ class HOScf(CharacteristicFunction):
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x = self.getDataArray(self.getCut())
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xnp =x[0].data
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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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if self.getOrder() == 3: # this is skewness
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print 'Calculating skewness ...'
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y = np.power(xnp, 3)
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@ -256,6 +284,9 @@ class HOScf(CharacteristicFunction):
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elif self.getOrder() == 4:
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LTA[j] = lta / np.power(lta1, 2)
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nn = np.isnan(LTA)
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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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@ -266,6 +297,9 @@ class ARZcf(CharacteristicFunction):
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print 'Calculating AR-prediction error from single trace ...'
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x = self.getDataArray(self.getCut())
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xnp = x[0].data
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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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#some parameters needed
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#add noise to time series
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xnoise = xnp + np.random.normal(0.0, 1.0, len(xnp)) * self.getFnoise() * max(abs(xnp))
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@ -288,6 +322,9 @@ class ARZcf(CharacteristicFunction):
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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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@ -376,6 +413,12 @@ class ARHcf(CharacteristicFunction):
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print 'Calculating AR-prediction error from both horizontal traces ...'
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xnp = self.getDataArray(self.getCut())
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n0 = np.isnan(xnp[0].data)
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if len(n0) > 1:
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xnp[0].data[n0] = 0
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n1 = np.isnan(xnp[1].data)
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if len(n1) > 1:
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xnp[1].data[n1] = 0
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#some parameters needed
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#add noise to time series
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@ -390,7 +433,7 @@ class ARHcf(CharacteristicFunction):
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cf = []
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loopstep = self.getARdetStep()
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for i in range(ldet + self.getOrder() - 3, tend - lpred + 1, loopstep[1]):
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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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#AR prediction of waveform using calculated AR coefficients
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self.arPredH(xnp, self.arpara, i + 1, lpred)
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@ -401,6 +444,9 @@ class ARHcf(CharacteristicFunction):
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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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def arDetH(self, data, order, rind, ldet):
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@ -493,6 +539,15 @@ class AR3Ccf(CharacteristicFunction):
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print 'Calculating AR-prediction error from all 3 components ...'
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xnp = self.getDataArray(self.getCut())
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n0 = np.isnan(xnp[0].data)
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if len(n0) > 1:
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xnp[0].data[n0] = 0
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n1 = np.isnan(xnp[1].data)
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if len(n1) > 1:
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xnp[1].data[n1] = 0
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n2 = np.isnan(xnp[2].data)
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if len(n2) > 1:
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xnp[2].data[n2] = 0
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#some parameters needed
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#add noise to time series
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@ -508,7 +563,7 @@ class AR3Ccf(CharacteristicFunction):
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cf = []
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loopstep = self.getARdetStep()
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for i in range(ldet + self.getOrder() - 3, tend - lpred + 1, loopstep[1]):
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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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#AR prediction of waveform using calculated AR coefficients
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self.arPred3C(xnp, self.arpara, i + 1, lpred)
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@ -520,6 +575,9 @@ class AR3Ccf(CharacteristicFunction):
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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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def arDet3C(self, data, order, rind, ldet):
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