math - what will be python code for runge kutta second method? -

is code ok?

def rkn(x, fx, n, hs):     k1 = []     k2 = []     k3 = []     k4 = []     xk = []     in range(n):         k1.append(fx[i](x)*hs)     in range(n):         xk.append(x[i] + k1[i]*0.5)     in range(n):         k2.append(fx[i](xk)*hs)     in range(n):         xk[i] = x[i] + k2[i]*0.5     in range(n):         k3.append(fx[i](xk)*hs)     in range(n):         xk[i] = x[i] + k3[i]     in range(n):         k4.append(fx[i](xk)*hs)     in range(n):         x[i] = x[i] + (k1[i] + 2*(k2[i] + k3[i]) + k4[i])/6     return x 

with numpy seems more readable:

code taken http://www.math-cs.gordon.edu/courses/mat342/python/diffeq.py

def rk2a( f, x0, t ):     """second-order runge-kutta method solve x' = f(x,t) x(t[0]) = x0.      usage:         x = rk2a(f, x0, t)      input:         f     - function of x , t equal dx/dt.  x may multivalued,                 in case should list or numpy array.  in                 case f must return numpy array same dimension                 x.         x0    - initial condition(s).  specifies value of x when                 t = t[0].  can either scalar or list or numpy array                 if system of equations being solved.         t     - list or numpy array of t values compute solution at.                 t[0] the initial condition point, , difference                 h=t[i+1]-t[i] determines step size h.      output:         x     - numpy array containing solution values corresponding each                 entry in t array.  if system being solved, x                 array of arrays.      notes:         version based on algorithm presented in "numerical         analysis", 6th edition, burden , faires, brooks-cole, 1997.     """      n = len( t )     x = numpy.array( [ x0 ] * n )     in xrange( n - 1 ):         h = t[i+1] - t[i]         k1 = h * f( x[i], t[i] ) / 2.0         x[i+1] = x[i] + h * f( x[i] + k1, t[i] + h / 2.0 )      return x