Is There An Equivalent To The Matlab Function Bsxfun In Python?

I'm trying to port some of my code from matlab to python, and some of it uses the bsxfun() function for virtual replication followed by multiplication or division (I also use it fo

Solution 1:

There isn't really an equivalent of bsxfun, that I'm aware of, although numpy does take care of a lot of broadcasting for you, as others mentioned.

This is commonly touted as an advantage of numpy over matlab, and it is true that a lot of broadcasting is simpler in numpy, but bsxfun is actually more general, because it can take user-defined functions.

Numpy has this: http://docs.scipy.org/doc/numpy/reference/generated/numpy.apply_along_axis.html but only for 1d.

Solution 2:

Python is very easy to use compared to matlab bsxfun(x) in python numpy can be easily done by ... in array[], e.g. m[...,:] You can try this:

>>>m = np.zeros([5,13], dtype=np.float32)
>>>print(m)

    [[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
     [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
     [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
     [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
     [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]

>>>c=np.array([[1,2,3,4,5,6,7,8,9,10,11,12,13]])
>>>print(m[...,:] +4*c)
[[  4.   8.  12.  16.  20.  24.  28.  32.  36.  40.  44.  48.  52.]
 [  4.   8.  12.  16.  20.  24.  28.  32.  36.  40.  44.  48.  52.]
 [  4.   8.  12.  16.  20.  24.  28.  32.  36.  40.  44.  48.  52.]
 [  4.   8.  12.  16.  20.  24.  28.  32.  36.  40.  44.  48.  52.]
 [  4.   8.  12.  16.  20.  24.  28.  32.  36.  40.  44.  48.  52.]]