Modified internal function getTimeArray for calculating correct time stamps even for AR prediction, where CF time array is different to data time array. Implemented getARdetStep and setARdetStep where ARdetStep is the recalculation step of AR coefficients.

pylot/core/pick/CharFuns.py

@@ -22,7 +22,7 @@ class CharacteristicFunction(object):
     '''
     SuperClass for different types of characteristic functions.
     '''
-    def __init__(self, data, cut, t2, order, t1=None, fnoise=0.001):
+    def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=0.001):
         '''
         Initialize data type object with information from the original
         Seismogram.
@@ -55,6 +55,7 @@ class CharacteristicFunction(object):
         self.setTime2(t2)
         self.setOrder(order)
         self.setFnoise(fnoise)
+        self.setARdetStep(t2)
         self.calcCF(self.getDataArray())
         self.arpara = np.array([])
         self.xpred = np.array([])
@@ -66,12 +67,14 @@ class CharacteristicFunction(object):
         t2:\t{t2}\n
         Order:\t\t{order}\n
         Fnoise:\t{fnoise}\n
+        ARdetStep:\t{ardetstep}\n
         '''.format(name=type(self).__name__,
                    cut=self.getCut(),
                    t1=self.getTime1(),
                    t2=self.getTime2(),
                    order=self.getOrder(),
-                   fnoise=self.getFnoise())
+                   fnoise=self.getFnoise(),
+                   ardetstep=self.getARdetStep())
 
     def getCut(self):
         return self.cut
@@ -91,11 +94,13 @@ class CharacteristicFunction(object):
     def setTime2(self, t2):
         self.t2 = t2
 
-    def getTimeArray(self):
-        incr = self.getIncrement()
-        timeArray = np.arange(0, len(self.getCF()) / incr**-1,
-                              incr) + self.getCut()[0]
-        return timeArray
+    def getARdetStep(self):
+        return self.ARdetStep
+
+    def setARdetStep(self, t1):
+        if t1:
+           self.ARdetStep = t1 / 4
+           return self.ARdetStep
 
     def getOrder(self):
         return self.order
@@ -109,6 +114,29 @@ class CharacteristicFunction(object):
         """
         return self.dt
 
+    def getTimeArray(self):
+        if self.getTime1() == None and self.getTime2() and self.getOrder():
+           #for HOS 
+           incr = self.getIncrement()
+           timeArray = np.arange(0, len(self.getCF()) * incr,
+                                 incr) + self.getCut()[0] 
+        elif self.getTime1() == None and self.getTime2() and self.getOrder() == None:
+           #for AIC-HOS
+           incr = self.getIncrement()
+           timeArray = np.arange(0, len(self.getCF()) * incr,
+                                 incr) + self.getCut()[0]  
+        elif self.getTime1() and self.getTime2() and self.getOrder() == 0:
+           #for AIC-AR
+           incr = self.getARdetStep()
+           timeArray = np.arange(0, len(self.getCF()) * incr,
+                                 incr) + self.getCut()[0] + self.getTime1() + self.getTime2()
+        elif self.getTime1() and self.getTime2() and self.getOrder():
+           #for AR
+           incr = self.getARdetStep()
+           timeArray = np.arange(0, len(self.getCF()) * incr,
+                                 incr) + self.getCut()[0] + self.getTime1() + self.getTime2()
+        return timeArray
+
     def getFnoise(self):
         return self.fnoise
 
@@ -190,7 +218,7 @@ class AICcf(CharacteristicFunction):
                         cumsumcf[k - 1]) / (datlen - k + 1)))
         cf[0] = cf[1]
         inf = np.isinf(cf)
-        ff = np.where(inf == 'True')
+        ff = np.where(inf == True)
         if len(ff) >= 1:
             cf[ff] = 0
 
@@ -260,7 +288,7 @@ class ARZcf(CharacteristicFunction):
         lpred = int(np.ceil(self.getTime2() / self.getIncrement())) #length of AR-prediction window [samples]
 
         cf = []
-        for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, lpred / 16):
+        for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, lpred / 4):
             #determination of AR coefficients
             self.arDetZ(xnoise, self.getOrder(), i-ldet, i)
             #AR prediction of waveform using calculated AR coefficients