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.tay...@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.tay...@utoronto.ca> wrote: > > I see. > > > > Should functionality like this be included in numpy? > > > > Jon. > > > > > > On Tue, Mar 8, 2011 at 3:39 PM, <josef.p...@gmail.com> wrote: > >> On Tue, Mar 8, 2011 at 3:03 PM, Jonathan Taylor > >> <jonathan.tay...@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 > >> > >>> _______________________________________________ > >>> NumPy-Discussion mailing list > >>> NumPy-Discussion@scipy.org > >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion > >>> > >> _______________________________________________ > >> NumPy-Discussion mailing list > >> NumPy-Discussion@scipy.org > >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > >> > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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