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[cleanup] in charfuns.py using code inspection

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This commit is contained in:
Darius Arnold 2017-10-05 16:52:36 +02:00
parent 9a8d7da0e6
commit 87b4ce1345
1 changed files with 13 additions and 12 deletions
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25
pylot/core/pick/charfuns.py
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@ -72,7 +72,7 @@ class CharacteristicFunction(object):
t2=self.getTime2(), t2=self.getTime2(),
order=self.getOrder(), order=self.getOrder(),
fnoise=self.getFnoise(), fnoise=self.getFnoise(),
ardetstep=self.getARdetStep[0]()) ardetstep=self.getARdetStep()[0]())
def getCut(self): def getCut(self):
return self.cut return self.cut
@ -233,7 +233,7 @@ class AICcf(CharacteristicFunction):
np.log((cumsumcf[datlen - 1] - cumsumcf[k - 1]) / (datlen - k + 1))) np.log((cumsumcf[datlen - 1] - cumsumcf[k - 1]) / (datlen - k + 1)))
cf[0] = cf[1] cf[0] = cf[1]
inf = np.isinf(cf) inf = np.isinf(cf)
ff = np.where(inf == True) ff = np.where(inf is True)
if len(ff) >= 1: if len(ff) >= 1:
cf[ff] = 0 cf[ff] = 0
@ -477,9 +477,9 @@ class ARHcf(CharacteristicFunction):
# AR prediction of waveform using calculated AR coefficients # AR prediction of waveform using calculated AR coefficients
self.arPredH(xnp, self.arpara, i + 1, lpred) self.arPredH(xnp, self.arpara, i + 1, lpred)
# prediction error = CF # prediction error = CF
cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \ cf[i + lpred] = 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[1][i:i + lpred] - xnp[1][i:i + lpred], 2)
2 * lpred)) ) / (2 * lpred))
nn = np.isnan(cf) nn = np.isnan(cf)
if len(nn) > 1: if len(nn) > 1:
cf[nn] = 0 cf[nn] = 0
@ -529,8 +529,8 @@ class ARHcf(CharacteristicFunction):
for i in range(rind, ldet): for i in range(rind, ldet):
ki = k - 1 ki = k - 1
ji = j - 1 ji = j - 1
A[ki, ji] = A[ki, ji] + data[0, i - ji] * data[0, i - ki] + data[1, i - ji] * data[1, i - ki] A[ki, ji] = A[ki, ji] + data[0, i - ji] * data[0, i - ki] \
+ data[1, i - ji] * data[1, i - ki]
A[ji, ki] = A[ki, ji] A[ji, ki] = A[ki, ji]
# apply Moore-Penrose inverse for SVD yielding the AR-parameters # apply Moore-Penrose inverse for SVD yielding the AR-parameters
@ -629,10 +629,10 @@ class AR3Ccf(CharacteristicFunction):
# AR prediction of waveform using calculated AR coefficients # AR prediction of waveform using calculated AR coefficients
self.arPred3C(xnp, self.arpara, i + 1, lpred) self.arPred3C(xnp, self.arpara, i + 1, lpred)
# prediction error = CF # prediction error = CF
cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \ cf[i + lpred] = 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[1][i:i + lpred] - xnp[1][i:i + lpred], 2)
+ np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / ( + np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)
3 * lpred)) ) / (3 * lpred))
nn = np.isnan(cf) nn = np.isnan(cf)
if len(nn) > 1: if len(nn) > 1:
cf[nn] = 0 cf[nn] = 0
@ -683,7 +683,8 @@ class AR3Ccf(CharacteristicFunction):
for i in range(rind, ldet): for i in range(rind, ldet):
ki = k - 1 ki = k - 1
ji = j - 1 ji = j - 1
A[ki, ji] = A[ki, ji] + data[0, i - ji] * data[0, i - ki] + data[1, i - ji] * data[1, i - ki] \ A[ki, ji] = A[ki, ji] + data[0, i - ji] * data[0, i - ki] \
+ data[1, i - ji] * data[1, i - ki] \
+ data[2, i - ji] * data[2, i - ki] + data[2, i - ji] * data[2, i - ki]
A[ji, ki] = A[ki, ji] A[ji, ki] = A[ki, ji]