(self,x)
return value,err
This routine applies the convergence acceleration to the individual terms.

        A = sum(a_k, k = 0..infinity)

        v, e = ...step(a_k)

        output:
          v      current estimate of the series A
          e      an error estimate which is simply the difference between the current
                 estimate and the last estimate.
This routine applies the convergence acceleration to the individual terms.
This routine applies the convergence acceleration to the individual terms .
def step(self,x):
        """
        This routine applies the convergence acceleration to the individual terms.

        A = sum(a_k, k = 0..infinity)

        v, e = ...step(a_k)

        output:
          v      current estimate of the series A
          e      an error estimate which is simply the difference between the current
                 estimate and the last estimate.
        """

        if self.variant!="v":
            if self.n==0:
                self.s=x
                self.run(self.s,x)
            else:
                self.s+=x
                self.run(self.s,x)
        else:
            if isinstance(self.last_s,bool):
                self.last_s=x
                self.s=0
                self.last=0
                return x,abs(x)

            self.s+=self.last_s
            self.run(self.s,self.last_s,x)
            self.last_s=x

        value=self.A[0]/self.B[0]
        err=abs(value-self.last)
        self.last=value

        return value,err
def step ( self , x ) : if self . variant != "v" : if self . n == 0 : self . s = x self . run ( self . s , x ) else : self . s += x self . run ( self . s , x ) else : if isinstance ( self . last_s , bool ) : self . last_s = x self . s = 0 self . last = 0 return x , abs ( x ) self . s += self . last_s self . run ( self . s , self . last_s , x ) self . last_s = x value = self . A [ 0 ] / self . B [ 0 ] err = abs ( value - self . last ) self . last = value return value , err