(self,t, N = 1)
return x
Generate the position of the process at time t > startTime
        parameters:
            N: number of samples
            t: position to sample at.
        returns:
            returns a (n,) np.array
Generate the position of the process at time t > startTime
        parameters:
            N: number of samples
            t: position to sample at.
        returns:
            returns a (n,) np.array
Generate the position of the process at time t > startTime parameters : N : number of samples t : position to sample at . returns : returns a ( n ) np . array
def _sample_position(self,t, N = 1):
        """
        Generate the position of the process at time t > startTime
        parameters:
            N: number of samples
            t: position to sample at.
        returns:
            returns a (n,) np.array 
        """
        v = np.zeros( N ) 
        iv = np.ones( N, dtype=bool )
        x = self.startPosition*np.ones( N) 
        continue_ = True
        i = 1
        t = t - self.startTime #should be positive.
        size = v.shape[0]
        while continue_:
            n_samples = iv.sum()
            v[iv] += self.T.rvs( n_samples )
            
            #if the next time is beyond reach, we do not add a new jump
            iv[ v >= t] = False
            n_samples = iv.sum()

            x[iv] += self.J.rvs( n_samples )

            size = iv.sum() #any left?
            empty = size == 0
            
            i+=1
            if empty:
                continue_ = False
                
            if i > 10000:
                warnings.warn("The algorithm did not converge. Be sure 'T' is a non negative random \
                                variable, or set 't' to a smaller value.")
                break
                
        return x
def _sample_position ( self , t , N = 1 ) : v = np . zeros ( N ) iv = np . ones ( N , dtype = bool ) x = self . startPosition * np . ones ( N ) continue_ = True i = 1 t = t - self . startTime #should be positive. size = v . shape [ 0 ] while continue_ : n_samples = iv . sum ( ) v [ iv ] += self . T . rvs ( n_samples ) #if the next time is beyond reach, we do not add a new jump iv [ v >= t ] = False n_samples = iv . sum ( ) x [ iv ] += self . J . rvs ( n_samples ) size = iv . sum ( ) #any left? empty = size == 0 i += 1 if empty : continue_ = False if i > 10000 : warnings . warn ( "The algorithm did not converge. Be sure 'T' is a non negative random \
                                variable, or set 't' to a smaller value." ) break return x