On Do, 2016-01-07 at 22:48 -0500, John Kirkham wrote: > First, off sorry for the long turnaround on responding to these > questions. Below I have tried to respond to everyone's questions and > comments. I have restructured the order of the messages so that my > responses are a little more structured. If anybody has more thoughts > or questions, please let me know. >
<snip> > From a performance standpoint, `expand_dims` using `reshape` to add > these extra dimensions. So, it has the possibility of not returning a > view, but a copy, which could take some time to build if the array is > large. By using our method, we guarantee a view will always be > returned; so, this allocation will never be encountered. Actually reshape can be used, if stride tricks can do the trick, then reshape is guaranteed to not do a copy. Still can't say I feel it is a worthy addition (but others may disagree), especially since I realized we have `expand_dims` already. I just am not sure it would actually be used reasonably often. But how about adding a `dims=1` argument to `expand_dims`, so that your function becomes at least a bit easier: newarr = np.expand_dims(arr, -1, after.ndim) # If manual broadcast is necessary: newarr = np.broadcast_to(arr, before.shape + arr.shape + after.shape) Otherwise I guess we could morph `expand_dims` partially to your function, though it would return a read-only view if broadcasting is active, so I am a bit unsure I like it. In other words, my personal currently preferred solution to get some of this would be to add a simple `dims` argument to `expand_dims` and add `broadcast_to` to its "See Also" section (also it could be in the `squeeze` see also section I think). To further help out other people, we could maybe even mention the combination in the examples (i.e. broadcast_to example?). - Sebastian > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion >
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