I have a numpy nd array. A simplified version of my task is to take a vector from along each axis. To illustrate:
import numpy
x = numpy.array(range(24)).reshape
As suggested from numpy
's documentation about indexing you can use the slice built-in function and tuple concatenation to create variable indexes.
In fact the :
in the subscript is simply the literal notation for a slice
literal.
In particular :
is equivalent to slice(None)
(which, itself, is equivalent to slice(None, None, None)
where the arguments are start
, stop
and step
).
For example:
a[(0,) * N + (slice(None),)]
is equivalent to:
a[0, 0, ..., 0, :] # with N zeros
The :
notation for slices can only be used directly inside a subscript. For example this fails:
In [10]: a[(0,0,:)]
File "", line 1
a[(0,0,:)]
^
SyntaxError: invalid syntax
To allow extracting a slice from an array of arbitrary dimensions you can write a simple function such as:
def make_index(num_dimension, slice_pos):
zeros = [0] * num_dimension
zeros[slice_pos] = slice(None)
return tuple(zeros)
And use it as in:
In [3]: a = np.array(range(24)).reshape((2, 3, 4))
In [4]: a[make_index(3, 2)]
Out[4]: array([0, 1, 2, 3])
In [5]: a[make_index(3, 1)]
Out[5]: array([0, 4, 8])
In [6]: a[make_index(3, 0)]
Out[6]: array([ 0, 12])
You can generalize make_index
to do any kind of things. The important thing to remember is that it should, in the end, return a tuple
containing either integers or slice
s.