Re: [Numpy-discussion] Slicing a numpy array and getting the complement
2008/5/19 Orest Kozyar [EMAIL PROTECTED]: Given a slice, such as s_[..., :-2:], is it possible to take the complement of this slice? Specifically, s_[..., ::-2]. I have a series of 2D arrays that I need to split into two subarrays via slicing where the members of the second array are all the members leftover from the slice. The problem is that the slice itself will vary, and could be anything such as s_[..., 1:4:] or s_[..., 1:-4:], etc, so I'm wondering if there's a straightforward idiom or routine in Numpy that would facilitate taking the complement of a slice? I've looked around the docs, and have not had much luck. If you are using boolean indexing, of course complements are easy (just use ~). But if you want slice indexing, so that you get views, sometimes the complement cannot be expressed as a slice: for example: A = np.arange(10) A[2:4] The complement of A[2:4] is np.concatenate((A[:2],A[4:])). Things become even more complicated if you start skipping elements. If you don't mind fancy indexing, you can convert your index arrays into boolean form: complement = A==A complement[idx] = False Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Slicing a numpy array and getting the complement
On Mon, May 19, 2008 at 9:34 AM, Orest Kozyar [EMAIL PROTECTED] wrote: Given a slice, such as s_[..., :-2:], is it possible to take the complement of this slice? Specifically, s_[..., ::-2]. Hmm, that doesn't look like the complement. Did you mean s_[..., -2:] and s_[..., :-2]? I have a series of 2D arrays that I need to split into two subarrays via slicing where the members of the second array are all the members leftover from the slice. The problem is that the slice itself will vary, and could be anything such as s_[..., 1:4:] or s_[..., 1:-4:], etc, so I'm wondering if there's a straightforward idiom or routine in Numpy that would facilitate taking the complement of a slice? I've looked around the docs, and have not had much luck. In general, for any given slice, there may not be a slice giving the complement. For example, the complement of arange(6)[1:4] should be array([0,4,5]), but there is no slice which can make that. Things get even more difficult with start:stop:step slices let alone simultaneous multidimensional slices. Can you be more specific as to exactly the variety of slices you need to support? -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Slicing a numpy array and getting the complement
If you don't mind fancy indexing, you can convert your index arrays into boolean form: complement = A==A complement[idx] = False This actually would work perfectly for my purposes. I don't really need super-fancy indexing. Given a slice, such as s_[..., :-2:], is it possible to take the complement of this slice? Specifically, s_[..., ::-2]. Hmm, that doesn't look like the complement. Did you mean s_[..., -2:] and s_[..., :-2]? Whoops, yes you're right. In general, for any given slice, there may not be a slice giving the complement. For example, the complement of arange(6)[1:4] should be array([0,4,5]), but there is no slice which can make that. Things get even more difficult with start:stop:step slices let alone simultaneous multidimensional slices. Can you be more specific as to exactly the variety of slices you need to support? I think Anne's solution will work well for what I need to do. Thanks! ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Slicing a numpy array and getting the complement
2008/5/19 Orest Kozyar [EMAIL PROTECTED]: If you don't mind fancy indexing, you can convert your index arrays into boolean form: complement = A==A complement[idx] = False This actually would work perfectly for my purposes. I don't really need super-fancy indexing. Heh. Actually fancy indexing is numpy-speak for indexing with anything that's not an integer or a slice. In this case, indexing with a boolean array is fancy indexing. The reason we make this distinction is that with slices, the new array you get is actually just a reference to the original array (so you can modify the original array through it). With fancy indexing, the new array you get is actually a copy. (Assigning to fancy-indexed arrays is handled specially in __setitem__, so it works.) Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
