[Numpy-discussion] Add New Functionality for Indexing Along an Axis to Numpy?

[John Salvatier]

The new iteration functionality will be providing this in the near future
(along with many other things). See
https://github.com/numpy/numpy/blob/master/doc/neps/new-iterator-ufunc.rst

On Mon, Mar 14, 2011 at 6:30 PM, Jonathan Taylor <
jonathan.taylor at utoronto.ca> wrote:

> Please excuse the double post as I suspect people who may have
> thoughts on the inclusion of such functionality in numpy were not
> following the discussion due to the old subject.  I am perfectly happy
> keeping this functionality locally but some of my colleagues have also
> indicated that they have resorted to loops in the past to solve this
> not uncommon use case so perhaps it would be helpful to more people if
> it (or something similar?) was included in numpy?
>
> Jonathan.
>
> On Thu, Mar 10, 2011 at 12:00 PM, Jonathan Taylor
> <jonathan.taylor at utoronto.ca> wrote:
> > I see.
> >
> > Should functionality like this be included in numpy?
> >
> > Jon.
> >
> >
> > On Tue, Mar 8, 2011 at 3:39 PM,  <josef.pktd at gmail.com> wrote:
> >> On Tue, Mar 8, 2011 at 3:03 PM, Jonathan Taylor
> >> <jonathan.taylor at utoronto.ca> wrote:
> >>> I am wanting to use an array b to index into an array x with dimension
> >>> bigger by 1 where the element of b indicates what value to extract
> >>> along a certain direction.  For example, b = x.argmin(axis=1).
> >>> Perhaps I want to use b to create x.min(axis=1) but also to index
> >>> perhaps another array of the same size.
> >>>
> >>> I had a difficult time finding a way to do this with np.take easily
> >>> and even with fancy indexing the resulting line is very complicated:
> >>>
> >>> In [322]: x.shape
> >>> Out[322]: (2, 3, 4)
> >>>
> >>> In [323]: x.min(axis=1)
> >>> Out[323]:
> >>> array([[ 2,  1,  7,  4],
> >>>       [ 8,  0, 15, 12]])
> >>>
> >>> In [324]: x[np.arange(x.shape[0])[:,np.newaxis,np.newaxis],
> >>> idx[:,np.newaxis,:], np.arange(x.shape[2])]
> >>> Out[324]:
> >>> array([[[ 2,  1,  7,  4]],
> >>>
> >>>       [[ 8,  0, 15, 12]]])
> >>>
> >>> In any case I wrote myself my own function for doing this (below) and
> >>> am wondering if this is the best way to do this or if there is
> >>> something else in numpy that I should be using? -- I figure that this
> >>> is a relatively common usecase.
> >>>
> >>> Thanks,
> >>> Jon.
> >>>
> >>> def mytake(A, b, axis):
> >>>    assert len(A.shape) == len(b.shape)+1
> >>>
> >>>    idx = []
> >>>    for i in range(len(A.shape)):
> >>>        if i == axis:
> >>>            temp = b.copy()
> >>>            shapey = list(temp.shape)
> >>>            shapey.insert(i,1)
> >>>        else:
> >>>            temp = np.arange(A.shape[i])
> >>>            shapey = [1]*len(b.shape)
> >>>            shapey.insert(i,A.shape[i])
> >>>        shapey = tuple(shapey)
> >>>        temp = temp.reshape(shapey)
> >>>        idx += [temp]
> >>>
> >>>    return A[tuple(idx)].squeeze()
> >>>
> >>>
> >>> In [319]: util.mytake(x,x.argmin(axis=1), 1)
> >>> Out[319]:
> >>> array([[ 2,  1,  7,  4],
> >>>       [ 8,  0, 15, 12]])
> >>>
> >>> In [320]: x.min(axis=1)
> >>> Out[320]:
> >>> array([[ 2,  1,  7,  4],
> >>>       [ 8,  0, 15, 12]])
> >>
> >> fewer lines but essentially the same thing and no shortcuts, I think
> >>
> >>>>> x= np.random.randint(5, size=(2, 3, 4))
> >>>>> x
> >> array([[[3, 1, 0, 1],
> >>        [4, 2, 2, 1],
> >>        [2, 3, 2, 2]],
> >>
> >>       [[2, 1, 1, 1],
> >>        [0, 2, 0, 3],
> >>        [2, 3, 3, 1]]])
> >>
> >>>>> idx = [np.arange(i) for i in x.shape]
> >>>>> idx = list(np.ix_(*idx))
> >>>>> idx[axis]=np.expand_dims(x.argmin(axis),axis)
> >>>>> x[idx]
> >> array([[[2, 1, 0, 1]],
> >>
> >>       [[0, 1, 0, 1]]])
> >>
> >>>>> np.squeeze(x[idx])
> >> array([[2, 1, 0, 1],
> >>       [0, 1, 0, 1]])
> >>
> >>>>> mytake(x,x.argmin(axis=1), 1)
> >> array([[2, 1, 0, 1],
> >>       [0, 1, 0, 1]])
> >>
> >> Josef
> >>
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