Re: [Numpy-discussion] Array slices and number of dimensions
On 1 September 2010 17:54, Thomas Robitaille wrote: > Hi, > > I'm trying to extract sub-sections of a multidimensional array while keeping > the number of dimensions the same. If I just select a specific element along > a given direction, then the number of dimensions goes down by one: > import numpy as np a = np.zeros((10,10,10)) a.shape > (10, 10, 10) a[0,:,:].shape > (10, 10) > > This makes sense to me. If I want to retain the initial number of dimensions, > I can do > a[[0],:,:].shape > (1, 10, 10) > > However, if I try and do this along two directions, I do get a reduction in > the number of dimensions: > a[[0],:,[5]].shape > (1, 10) > > I'm wondering if this is normal, or is a bug? In fact, I can get what I want > by doing: > a[[0],:,:][:,:,[5]].shape > (1, 10, 1) > > so I can get around the issue, but just wanted to check whether the issue > with a[[0],:,[5]] is a bug? No, it's not a bug. The key problem is that supplying lists does not extract a slice - it uses fancy indexing. This implies, among other things, that the data must be copied. When you supply two lists, that means something very different in fancy indexing. When you are supplying arrays in all index slots, what you get back has the same shape as the arrays you put in; so if you supply one-dimensional lists, like A[[1,2,3],[1,4,5],[7,6,2]] what you get is [A[1,1,7], A[2,4,6], A[3,5,2]] When you supply slices in some slots, what you get is complicated, and maybe not well-defined. In particular, I think the fancy-indexing dimensions always wind up at the front, and any slice dimensions are left at the end. In short, fancy indexing is not the way to go with your problem. I generally use np.newaxis: a[7,np.newaxis,:,8,np.newaxis] but you can also use slices of length one: a[7:8, :, 8:9] Anne > Thanks, > > Tom > > > ___ > 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
Re: [Numpy-discussion] Array slices and number of dimensions
Wed, 01 Sep 2010 17:54:26 -0400, Thomas Robitaille wrote: > >>> a[[0],:,:].shape > (1, 10, 10) [clip] > so I can get around the issue, but just wanted to check whether the > issue with a[[0],:,[5]] is a bug? No. The syntax does not mean what you think it means, see http://docs.scipy.org/doc/numpy/user/basics.indexing.html#indexing-multi-dimensional-arrays http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing -- Pauli Virtanen ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Array slices and number of dimensions
Thomas Robitaille wrote: > Hi, > > I'm trying to extract sub-sections of a multidimensional array while keeping > the number of dimensions the same. If I just select a specific element along > a given direction, then the number of dimensions goes down by one: > > > > In fact, I can get what I want by doing: > > a[[0],:,:][:,:,[5]].shape > (1, 10, 1) > > so I can get around the issue You can also use "trivial" slices: In [2]: a = np.zeros((10,10,10)) In [3]: a.shape Out[3]: (10, 10, 10) In [4]: a[0:1, :, 5:6].shape Out[4]: (1, 10, 1) Warren > , but just wanted to check whether the issue with a[[0],:,[5]] is a bug? > > Thanks, > > Tom > > > ___ > 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] Array slices and number of dimensions
Hi, I'm trying to extract sub-sections of a multidimensional array while keeping the number of dimensions the same. If I just select a specific element along a given direction, then the number of dimensions goes down by one: >>> import numpy as np >>> a = np.zeros((10,10,10)) >>> a.shape (10, 10, 10) >>> a[0,:,:].shape (10, 10) This makes sense to me. If I want to retain the initial number of dimensions, I can do >>> a[[0],:,:].shape (1, 10, 10) However, if I try and do this along two directions, I do get a reduction in the number of dimensions: >>> a[[0],:,[5]].shape (1, 10) I'm wondering if this is normal, or is a bug? In fact, I can get what I want by doing: >>> a[[0],:,:][:,:,[5]].shape (1, 10, 1) so I can get around the issue, but just wanted to check whether the issue with a[[0],:,[5]] is a bug? Thanks, Tom ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
