arrays - How to use vectorization instead of for loop to improve efficiency in python?

Question

I've been having trouble with the run time of my code as it is taking too long. I imagine this is because I am using for loops which is what I'm used to in MATLAB where for loops seems to be quicker than in Python. I've been trying for a while to use NumPy vectorization methods to do this instead as I have read this is better in terms of efficiency. However, I seem to be having issues implementing this. I would very much appreciate any guidance on how to do this.

The loop I am trying to do this on is

        for i in range(100000):
         theta = theta_rec[:][i]
         A = np.zeros((50,50))

          for k in range(50):
            for j in range(50):
              A[k,j] = math.sin(theta[j] - theta[k])

          B = -1 * np.array(sum(A))
          d_theta = 0.001*(5 + B)
          theta[(i+1),:] = theta_rec[i,:] + d_theta

          for m in range(50):
            D[(i+1),m] = theta_rec[(i+1),m]%(2.0*math.pi)

where theta_rec is a 100001x50 array. As you can see this deeply nested for loop isn't good for runtime and is acting as a bottleneck in my code.

In other words, I want to replace this for loop with some sort of NumPy vectorization function as this will improve run time performance. In particular, I think the inner 2 loops would benefit from this.

Any advice would be greatly appreciated!

Thank you!

question from:https://stackoverflow.com/questions/66051797/how-to-use-vectorization-instead-of-for-loop-to-improve-efficiency-in-python

Answer 1

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