python – What does [:, :] mean on NumPy arrays

python – What does [:, :] mean on NumPy arrays

The [:, :] stands for everything from the beginning to the end just like for lists. The difference is that the first : stands for first and the second : for the second dimension.

a = numpy.zeros((3, 3))

In [132]: a
Out[132]: 
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])

Assigning to second row:

In [133]: a[1, :] = 3

In [134]: a
Out[134]: 
array([[ 0.,  0.,  0.],
       [ 3.,  3.,  3.],
       [ 0.,  0.,  0.]])

Assigning to second column:

In [135]: a[:, 1] = 4

In [136]: a
Out[136]: 
array([[ 0.,  4.,  0.],
       [ 3.,  4.,  3.],
       [ 0.,  4.,  0.]])

Assigning to all:

In [137]: a[:] = 10

In [138]: a
Out[138]: 
array([[ 10.,  10.,  10.],
       [ 10.,  10.,  10.],
       [ 10.,  10.,  10.]])

numpy uses tuples as indexes.
In this case, this is a detailed slice assignment.

[0]     #means line 0 of your matrix
[(0,0)] #means cell at 0,0 of your matrix
[0:1]   #means lines 0 to 1 excluded of your matrix
[:1]    #excluding the first value means all lines until line 1 excluded
[1:]    #excluding the last param mean all lines starting form line 1 
         included
[:]     #excluding both means all lines
[::2]   #the addition of a second : is the sampling. (1 item every 2)
[::]    #exluding it means a sampling of 1
[:,:]   #simply uses a tuple (a single , represents an empty tuple) instead 
         of an index.

It is equivalent to the simpler

self.activity[:] = self.h

(which also works for regular lists as well)

python – What does [:, :] mean on NumPy arrays

This is slice assignment. Technically, it calls1

self.activity.__setitem__((slice(None,None,None),slice(None,None,None)),self.h)

which sets all of the elements in self.activity to whatever value self.h is storing. The code you have there really seems redundant. As far as I can tell, you could remove the addition on the previous line, or simply use slice assignment:

self.activity = numpy.zeros((512,512)) + self.h

or

self.activity = numpy.zeros((512,512))
self.activity[:,:] = self.h

Perhaps the fastest way to do this is to allocate an empty array and .fill it with the expected value:

self.activity = numpy.empty((512,512))
self.activity.fill(self.h)

1Actually, __setslice__ is attempted before calling __setitem__, but __setslice__ is deprecated, and shouldnt be used in modern code unless you have a really good reason for it.

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