Are you trying to get this:
>>> a = np.array([[1, 2, 3, 3, 2, 1], [4, 5, 6, 7, 8, 9]], dtype=float)
>>> np.array([np.power(a, -6), np.power(a, -8)])
array([[[ 1.00000000e+00, 1.56250000e-02, 1.37174211e-03,
1.37174211e-03, 1.56250000e-02, 1.00000000e+00],
[ 2.44140625e-04, 6.40000000e-05, 2.14334705e-05,
8.49985975e-06, 3.81469727e-06, 1.88167642e-06]],
[[ 1.00000000e+00, 3.90625000e-03, 1.52415790e-04,
1.52415790e-04, 3.90625000e-03, 1.00000000e+00],
[ 1.52587891e-05, 2.56000000e-06, 5.95374181e-07,
1.73466526e-07, 5.96046448e-08, 2.32305731e-08]]])
Power doesn't work that way. According to the in-built doc, you can either
- Raise the whole array to a single power, as in
np.power(a, 3), or
- Raise one array by different powers, as in
np.power(a, b), where b is an array with the same shape as a, or
Raise one array to many powers, as in np.power(a, b), where b has "at least" the shape of a, with extra dimensions containing different powers (as I understand it, anyway). So, for your case, the following would also produce the same result:
>>> b = np.array([-6 * np.ones(a.shape), -8 * np.ones(a.shape)])
>>> b
array([[[-6., -6., -6., -6., -6., -6.],
[-6., -6., -6., -6., -6., -6.]],
[[-8., -8., -8., -8., -8., -8.],
[-8., -8., -8., -8., -8., -8.]]])
>>> np.power(a, b)