np.negative is silghtly faster than multiplying (as it is a ufunc)
N = 5
arr = np.arange(N ** 3).reshape(N, N, N)
%timeit arr.ravel()[1::2] *= -1
%timeit np.negative(arr.ravel()[1::2], out = arr.ravel()[1::2])
The slowest run took 8.74 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 3.39 µs per loop
The slowest run took 5.57 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 3.12 µs per loop
N = 25
arr = np.arange(N ** 3).reshape(N, N, N)
%timeit arr.ravel()[1::2] *= -1
%timeit np.negative(arr.ravel()[1::2], out = arr.ravel()[1::2])
The slowest run took 7.03 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 10.8 µs per loop
The slowest run took 5.27 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 8.63 µs per loop
N = 101
arr = np.arange(N ** 3).reshape(N, N, N)
%timeit arr.ravel()[1::2] *= -1
%timeit np.negative(arr.ravel()[1::2], out = arr.ravel()[1::2])
1000 loops, best of 3: 663 µs per loop
1000 loops, best of 3: 512 µs per loop