sdeint/wiener.py
def deltaW(N, m, h):
    """Generate sequence of Wiener increments for m independent Wiener
    processes W_j(t) j=0..m-1 for each of N time intervals of length h.    

    Returns:
      dW (array of shape (N, m)): The [n, j] element has the value
      W_j((n+1)*h) - W_j(n*h) 
    """
    return np.random.normal(0., np.sqrt(h), (N, m))
    # return levy.rvs(0., 1e-11, (N, m))+np.random.normal(0., np.sqrt(h), (N, m))              #levy distribution
    # return cauchy.rvs(0., 1e-4, (N, m))                                                      #Cauchy distribution
Generate sequence of Wiener increments for m independent Wiener
    processes W_j(t) j=0..m-1 for each of N time intervals of length h.    

    Returns:
      dW (array of shape (N, m)): The [n, j] element has the value
      W_j((n+1)*h) - W_j(n*h) 
    
Generate sequence of Wiener increments for m independent Wiener
processes W_j(t) j=0..m-1 for each of N time intervals of length h.
Generate sequence of Wiener increments for m independent Wiener processes W_j ( t ) j = 0 .. m - 1 for each of N time intervals of length h .
def deltaW(N, m, h):
    return np.random.normal(0., np.sqrt(h), (N, m))
def deltaW ( N , m , h ) : return np . random . normal ( 0. , np . sqrt ( h ) , ( N , m ) )
Generate sequence of Wiener increments for m independent Wiener
processes W_j(t) j=0..m-1 for each of N time intervals of length h.
Generate sequence of Wiener increments for m independent Wiener processes W_j ( t ) j = 0 .. m - 1 for each of N time intervals of length h .
"""Generate sequence of Wiener increments for m independent Wiener
    processes W_j(t) j=0..m-1 for each of N time intervals of length h.    

    Returns:
      dW (array of shape (N, m)): The [n, j] element has the value
      W_j((n+1)*h) - W_j(n*h) 
    """ # return levy.rvs(0., 1e-11, (N, m))+np.random.normal(0., np.sqrt(h), (N, m))              #levy distribution # return cauchy.rvs(0., 1e-4, (N, m))                                                      #Cauchy distribution
dW (array of shape (N, m)): The [n, j] element has the value
W_j((n+1)*h) - W_j(n*h)
dW ( array of shape ( N m )) : The [ n j ] element has the value W_j (( n + 1 ) * h ) - W_j ( n * h )
N
m
h