No speed gains from Cython

Question

I am trying to define a function that contains an inner loop for simulating an integral.

The problem is speed. Evaluating the function once can take up to 30 seconds on

Answer 1

Taking the advice given here, I have spent more time profiling the above code. To hopefully clean things up a bit I defined

I have profiled the code a bit more and have a better idea of which pieces are the slowest. I additionally defined

X = data[:, 2:7]
m_y = data[:, 21].reshape(J,1)
sigma_y = 1.0
price = data[:, 7].reshape(J, 1)
shares_data = data[:,8]

Then it is the following lines that are eating up most of the total time.

mu_ij = np.dot((X*np.array([s1, s2, s3, s4, s5])), nu[1:K+1,:])
mu_y  = a * np.log(np.exp(m_y + sigma_y*nu[0,:].reshape(1,ns)) - price)
V = delta.reshape(J,1) + mu_ij + mu_y
exp_vi = np.exp(V)
P_i = (1.0 / np.sum(exp_vi[np.where(data[:,1]==71)], 0)) *  exp_vi[np.where(data[:,1]==71)] 
for yr in xarange(19):
    P_yr = (1.0 / np.sum(exp_vi[np.where(data[:,1]==yr)], 0)) * exp_vi[np.where(data[:,1]==yr)]
P_i  = np.concatenate((P_i, P_yr))

I get the impression this is an overly cumbersome way to achieve my goal. I was hoping somebody might be able to provide some advice on how to speed these lines up. Maybe there are Numpy capabilities I am missing? If this problem is not sufficiently well specified for you to be helpful, I would be happy to provide more details on the context of my problem. Thanks!