Is there a numpy function to divide an array along an axis with elements from another array? For example, suppose I have an array a with shape (l,m,n) and an array b with shape (m,); I'm looking for something equivalent to:
def divide_along_axis(a,b,axis=None):
if axis is None:
return a/b
c = a.copy()
for i, x in enumerate(c.swapaxes(0,axis)):
x /= b[i]
return c
For example, this is useful when normalizing an array of vectors:
>>> a = np.random.randn(4,3)
array([[ 1.03116167, -0.60862215, -0.29191449],
[-1.27040355, 1.9943905 , 1.13515384],
[-0.47916874, 0.05495749, -0.58450632],
[ 2.08792161, -1.35591814, -0.9900364 ]])
>>> np.apply_along_axis(np.linalg.norm,1,a)
array([ 1.23244853, 2.62299312, 0.75780647, 2.67919815])
>>> c = divide_along_axis(a,np.apply_along_axis(np.linalg.norm,1,a),0)
>>> np.apply_along_axis(np.linalg.norm,1,c)
array([ 1., 1., 1., 1.])
Answer
For the specific example you've given: dividing an (l,m,n) array by (m,) you can use np.newaxis:
a = np.arange(1,61, dtype=float).reshape((3,4,5)) # Create a 3d array
a.shape # (3,4,5)
b = np.array([1.0, 2.0, 3.0, 4.0]) # Create a 1-d array
b.shape # (4,)
a / b # Gives a ValueError
a / b[:, np.newaxis] # The result you want
You can read all about the broadcasting rules here. You can also use newaxis more than once if required. (e.g. to divide a shape (3,4,5,6) array by a shape (3,5) array).
From my understanding of the docs, using newaxis + broadcasting avoids also any unecessary array copying.
Indexing, newaxis etc are described more fully here now. (Documentation reorganised since this answer first posted).