numpy only perform function on nonzero parts while preserving structure of array

Question

In numpy:

Foo = 
array([[ 3.5,  0. ,  2.5,  2. ,  0. ,  1. ,  0. ],
       [ 0. ,  3. ,  2.5,  2. ,  0. ,  0. ,  0.5],
       [ 3.5,  0. ,  0. ,  0. ,  1.5,  0. ,  0.5]])

I want to perform a function on Foo such that only the nonzero elements are changed, i.e. for f(x) = x(nonzero)+5:

array([[ 8.5,  0. ,  7.5,  7. ,  0. ,  6. ,  0. ],
       [ 0. ,  8. ,  8.5,  7. ,  0. ,  0. ,  5.5],
       [ 8.5,  0. ,  0. ,  0. ,  6.5,  0. ,  5.5]])

Also I want the shape/structure of the array to stay the same, so I don't think Foo[np.nonzero(Foo)] is going to work...

How do I do this in numpy?

thanks!

Answer 1

In [138]: foo = np.array([[ 3.5,  0. ,  2.5,  2. ,  0. ,  1. ,  0. ],
                          [ 0. ,  3. ,  2.5,  2. ,  0. ,  0. ,  0.5],
                          [ 3.5,  0. ,  0. ,  0. ,  1.5,  0. ,  0.5]])
In [141]: mask = foo != 0

In [142]: foo[mask] = foo[mask]+5

In [143]: foo
Out[143]: 
array([[ 8.5,  0. ,  7.5,  7. ,  0. ,  6. ,  0. ],
       [ 0. ,  8. ,  7.5,  7. ,  0. ,  0. ,  5.5],
       [ 8.5,  0. ,  0. ,  0. ,  6.5,  0. ,  5.5]])

Answer 2

you can also do it in place as follows

>>> import numpy as np
>>> foo = np.array([[ 3.5,  0. ,  2.5,  2. ,  0. ,  1. ,  0. ],
...                           [ 0. ,  3. ,  2.5,  2. ,  0. ,  0. ,  0.5],
...                           [ 3.5,  0. ,  0. ,  0. ,  1.5,  0. ,  0.5]])
>>> foo[foo!=0] += 5
>>> foo
array([[ 8.5,  0. ,  7.5,  7. ,  0. ,  6. ,  0. ],
       [ 0. ,  8. ,  7.5,  7. ,  0. ,  0. ,  5.5],
       [ 8.5,  0. ,  0. ,  0. ,  6.5,  0. ,  5.5]])
>>>