Question

I am trying to find how to apply two functions to a numpy array each one only on half the values.
Here is the code I have been trying

def hybrid_array(xs,height,center,fwhh):
    xs[xs<=center] = height*np.exp((-(xs[xs<=center]-center)**2)/(2*(fwhh/(2*np.sqrt(2*np.log(2))))**2))
    xs[xs>center] = height*1/(np.abs(1+((center-xs[xs>center])/(fwhh/2))**2))
    return xs

However I am overwriting the initial array that is passed to the argument. The usual trick of copying it with a slice ie. the following still changes b.

a = b[:]
c = hybrid_array(a,args)

If there is a better way of doing any part of what I am trying, I would be very grateful if you could let me know as I am still new to numpy arrays.
Thank you

Answer 1

Try copy.deepcopy to copy the array b onto a before calling your function.

import copy
a = copy.deepcopy(b)
c = hybrid_array(a,args)

Alternatively, you can use the copy method of the array

a = b.copy()
c = hybrid_array(a,args)

Note***

You may be wondering, why despite an easier way to copy an array with the copy method of numpy array I introduced the copy.deepcopy. Other’s may disagree but here is my reasoning

1. Using the method deepcopy makes it clear that you are intending to do a deepcopy instead of reference copy
2. All python’s data type do not support the copy method. Numpy has it and good it has but when you are programming with numpy and python you may end up using various numpy and non numpy data types not all of which would support the copy method. To remain consistent I would prefer to use the first.