Mike - 10 months ago 68

Python Question

Is there a way for numpy to ensure that an array operation mapping to repeated positions undergo a reduction, i.e. they are both performed on the result of each other?

`a = numpy.zeros([4], int) # [0 0 0 0]`

b = numpy.arange(0, 8) # [0 1 2 3 4 5 6 7]

positions = [0, 0, 1, 1, 2, 2, 3, 3]

a[positions] += b

# desired result: [0 + 1, 2 + 3, 4 + 5, 6 + 7]

# actual result: random crossover between [0, 2, 4, 6] and [1, 3, 5, 7]

as you can see both element 1 and 2 of b map to position 1 and so on, I need to make sure that += adds both, whereas by default it looks like it can randomly add 1 or 2 to zero at the same time,

Answer Source

When there are repeated indices, the behavior of in-place addition in a numpy array is undefined. To ensure the behavior that you want, use `numpy.add.at`

. (All numpy "ufuncs" have the `at`

method.)

For example, here are your arrays:

```
In [21]: a
Out[21]: array([0, 0, 0, 0])
In [22]: b
Out[22]: array([0, 1, 2, 3, 4, 5, 6, 7])
In [23]: positions
Out[23]: [0, 0, 1, 1, 2, 2, 3, 3]
```

Use `numpy.add.at`

to accumulate the values:

```
In [24]: np.add.at(a, positions, b)
In [25]: a
Out[25]: array([ 1, 5, 9, 13])
```