You want index
to be a tuple, with a mix of numbers, lists and slice
objects. A number of the numpy
functions that take an axis
parameter construct such a tuple.
A[(slice(None, None, None), 3, 4)] # == A[:, 3, 4]
there are various ways constructing that tuple:
index = (slice(None),)+(3,4)
index = [slice(None)]*3; index[1] = 3; index[2] = 4
index = np.array([slice(None)]*3]; index[1:]=[3,4]; index=tuple(index)
In this case index
can be list or tuple. It just can't be an array.
Starting with a list (or array) is handy in that you can modify values, but it is best to convert it to a tuple before use. I'd have to check the docs for the details, but there are circumstances where a list means something different from a tuple.
http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
Remember that a slicing tuple can always be constructed as obj and used in the x[obj] notation. Slice objects can be used in the construction in place of the [start:stop:step] notation. For example, x[1:10:5,::-1] can also be implemented as obj = (slice(1,10,5), slice(None,None,-1)); x[obj] . This can be useful for constructing generic code that works on arrays of arbitrary dimension.