[SciPy-User] Optimizing odeint without a for loop

[Barrett B]

This is an initial attempt to model a neural network with two differential
variables per cell--voltage, and the response variable n. I can go back and
add more later as needed. I have a question about this code (obviously, all
constants are given):

--------

#ODE

def f(X, t):

   N = len(X)/2

   dV = np.zeros(N); dn = np.zeros(N)

   for i in range(N):

   E = X[i]; n = X[N+i] #dummy

      n_inf_E = 1/(1 + np.exp((E_half_n - E)/k_n))

      m_inf_E = 1/(1 + np.exp((E_half_m - E)/k_m))

      dV[i] = (I - g_L*(E - E_L) - g_Na*m_inf_E*(E - E_Na) - g_K*n*(E -
E_K))/C

      for j in range(N):

          dV[i] += eps*connex[j, i]*X[j]

      dn[i] = (n_inf_E - n)/tau

    return np.concatenate((dV, dn))


connex = np.matrix([[-1,1,0],

[1,-2,1],

[0,1,-1]]) #connection matrix


t = np.arange(0, stopTime, timeStep)

V0 = np.array([0, -20, -50])

n0 = np.array([0.2, 0.4, 0.7]); N = len(n0)


soln = odeint(f, np.concatenate((V0, n0)), t)

-----------------

Is there a way to do this without the "for i in range (N)" loop; i.e., can
I run through all the dV[i]'s in a more efficient method?
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.scipy.org/pipermail/scipy-user/attachments/20140604/b733ee76/attachment.html>