Cannot modify 1d numpy array passed as argument with loop - arrays

Question

I am having a headache with a numba loop and a 1d numpy array and I cannot seem to find an explanation.
Basically, my goal is to pass to modify a numpy array in parallel with numba loop using a function both passed as arguments. It works well with a 2d numpy array but for some reasons, it does not with a simple 1d numpy array. This is the code to reproduce the issue:
import numpy as np
import numba as nb
size = 10
# Define a 1d numpy array
vec_test_1 = np.zeros(size)
# Fill the 1d array with values
for i in range(size):
vec_test_1[i] = float(i)
# Function that modifies and element
#nb.jit(nopython = True)
def func1(xyz):
xyz = xyz + 2.47
# Loop with numba to modify all elements of the array
#nb.jit(nopython = True, parallel = True)
def loop_numba(vec, func):
for i in nb.prange(len(vec)):
func(vec[i])
loop_numba(vec_test_1, func1)
The vec_test_1 is unchanged after this loop:
array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
when it should be:
array([ 2.47, 3.47, 4.47, 5.47, 6.47, 7.47, 8.47, 9.47, 10.47,
11.47])
What surprises me is that it works well when the array that is passed as argument is a 2d array. I am able to modify all its element with the numba loop.
Could anyone help me to understand this issue?

Answer 1

You have to define a return value since you make a copy of the individual elements when passing to the function.
Explanation: Found here.
Basically: you pass a single, immutable element to the function, so it is passed by copy (a copy is created, which is changed in the function). If you do it with a 2D array, to python this is a mutable object, so it is passed by reference. If you operate on it now, the underlying reference is changed, and this is visible in the result outside of the function.
import numpy as np
import numba as nb
size = 10
# Define a 1d numpy array
vec_test_1 = np.arange(size, dtype=np.float32)
# Function that modifies and element
#nb.jit(nopython = True)
def func1(xyz):
xyz = xyz + 2.47
return xyz
# Loop with numba to modify all elements of the array
#nb.jit(nopython = True, parallel = True)
def loop_numba(vec, func):
for i in nb.prange(len(vec)):
vec[i] = func(vec[i])
loop_numba(vec_test_1, func1)
In [2]: vec_test_1
Out[2]:
array([ 2.47, 3.47, 4.47, 5.47, 6.47, 7.47, 8.47, 9.47, 10.47,
11.47], dtype=float32)
Also: I changed your vector initialization to np.arange(size, dtype=float) to make it easier to understand.