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
You could compose an string with the code selecting the dimension you want and use eval to execute that code string.
An start is:
n = 2
sel = "0,"*(n-1) + ":"
eval('x[' + sel + ']')
To get exactly what you want, thinks are a little bit more complicated (but not so much):
ind = 2
n = 3
sel = "".join([ ("0" if i != ind else ":") + ("," if i < n-1 else "") for i in xrange(n)])
eval('x[' + sel + ']')
It is the same strategy that is used for Dynamic SQL.
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 "<ipython-input-10-f41b33bd742f>", 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.