Hi everyone,
>>> import numpy as np
>>> np.__version__
'1.9.0'
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
[array([[ 2., 2., -1.],
[ 2., 2., -1.]]), array([[-0.5, 2.5, 5.5],
[ 1. , 1. , 1. ]])]
On the other hand:
>>> import numpy as np
>>> np.__version__
'1.8.2'
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
[array([[ 2., 2., -1.],
[ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ],
[ 1. , 1. , 1. ]])]
For what it's worth, the 1.8 version of this function seems to be in
agreement with the Matlab equivalent function ('gradient'):
>> gradient([[1, 2, 6]; [3, 4, 5]])
ans =
1.0000 2.5000 4.0000
1.0000 1.0000 1.0000
This seems like a regression to me, but maybe it's an improvement?
Cheers,
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