iterate over two numpy arrays return 1d array

Question

I often have a function that returns a single value such as a maximum or integral. I then would like to iterate over another parameter. Here is a trivial example using a parabolic. I don't think its broadcasting since I only want the 1D array. In this case its maximums. A real world example is the maximum power point of a solar cell as a function of light intensity but the principle is the same as this example.

import numpy as np
x = np.linspace(-1,1) # sometimes this is read from file
parameters = np.array([1,12,3,5,6]) 
maximums = np.zeros_like(parameters)

for idx, parameter in enumerate(parameters):
    y = -x**2 + parameter
    maximums[idx] = np.max(y) # after I have the maximum I don't need the rest of the data.

print(maximums)

What is the best way to do this in Python/Numpy? I know one simplification is to make the function a def and then use np.vectorize but my understanding is it doesn't make the code any faster.

Did the posted solution work for you? Consider accepting it, if it did. More info - stackoverflow.com/help/someone-answers. — Divakar
– Divakar, Commented Nov 11, 2017 at 19:23

Divakar · Accepted Answer · 2017-11-09 16:53:55Z

1

Extend one of those arrays to 2D and then let broadcasting do those outer additions in a vectorized way -

maximums = (-x**2 + parameters[:,None]).max(1).astype(parameters.dtype)

Alternatively, with the explicit use of the outer addition method -

np.add.outer(parameters, -x**2).max(1).astype(parameters.dtype)

answered Nov 9, 2017 at 16:53

Divakar

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