In my case, what it does is:
A.shape = (5760,) A[none] -> (1, 5760)
In my case, use of none here is just a mistake. But why would you want this to be accepted at all, and how should it be interpreted?
Neal Becker wrote:
In my case, what it does is:
A.shape = (5760,) A[none] -> (1, 5760)
In my case, use of none here is just a mistake. But why would you want this to be accepted at all, and how should it be interpreted?
Actually, in my particular case, if it just acted as a noop, returning the original array, that would have been perfect. No idea if that's a good result in general.
On Do, 2015-12-31 at 11:36 -0500, Neal Becker wrote:
Neal Becker wrote:
In my case, what it does is:
A.shape = (5760,) A[none] -> (1, 5760)
In my case, use of none here is just a mistake. But why would you want this to be accepted at all, and how should it be interpreted?
Actually, in my particular case, if it just acted as a noop, returning the original array, that would have been perfect. No idea if that's a good result in general.
We have `np.newaxis` with `np.newaxis is None` for the same thing. `None` inserts a new axes, it is documented to do so in the indexing documentation, so I will ask you to check it if you have more questions. If you want a noop, you should probably use `...` or `Ellipsis`.
- Sebastian
NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
Slicing with None adds a new dimension. It's a common paradigm, though usually you'd use A[np.newaxis] or A[np.newaxis, ...] instead for readibility. (np.newaxis is None, but it's a lot more readable)
There's a good argument to be made that slicing with a single None shouldn't add a new axis, and only the more readable forms like A[None, :], A[..., None], etc should.
However, that would rather seriously break backwards compatibility. There's a fair amount of existing code that assumes "A[None]" prepends a new axis.
On Thu, Dec 31, 2015 at 10:36 AM, Neal Becker ndbecker2@gmail.com wrote:
Neal Becker wrote:
In my case, what it does is:
A.shape = (5760,) A[none] -> (1, 5760)
In my case, use of none here is just a mistake. But why would you want this to be accepted at all, and how should it be interpreted?
Actually, in my particular case, if it just acted as a noop, returning the original array, that would have been perfect. No idea if that's a good result in general.
NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
TBH, I wouldn't have expected it to work, but now that I see it, it does make some sense. I would have thought that it would error out as being ambiguous (prepend? append?). I have always used ellipses to make it explicit where the new axis should go. But, thinking in terms of how regular indexing works, I guess it isn't all that ambiguous.
Ben Root
On Thu, Dec 31, 2015 at 11:56 AM, Joe Kington joferkington@gmail.com wrote:
Slicing with None adds a new dimension. It's a common paradigm, though usually you'd use A[np.newaxis] or A[np.newaxis, ...] instead for readibility. (np.newaxis is None, but it's a lot more readable)
There's a good argument to be made that slicing with a single None shouldn't add a new axis, and only the more readable forms like A[None, :], A[..., None], etc should.
However, that would rather seriously break backwards compatibility. There's a fair amount of existing code that assumes "A[None]" prepends a new axis.
On Thu, Dec 31, 2015 at 10:36 AM, Neal Becker ndbecker2@gmail.com wrote:
Neal Becker wrote:
In my case, what it does is:
A.shape = (5760,) A[none] -> (1, 5760)
In my case, use of none here is just a mistake. But why would you want this to be accepted at all, and how should it be interpreted?
Actually, in my particular case, if it just acted as a noop, returning the original array, that would have been perfect. No idea if that's a good result in general.
NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
On Thu, Dec 31, 2015 at 12:10 PM, Benjamin Root ben.v.root@gmail.com wrote:
TBH, I wouldn't have expected it to work, but now that I see it, it does make some sense. I would have thought that it would error out as being ambiguous (prepend? append?). I have always used ellipses to make it explicit where the new axis should go. But, thinking in terms of how regular indexing works, I guess it isn't all that ambiguous.
Yeah, I'm not really a fan of the rule that indexing with too-few axes implicitly adds a "..." on the right
A[0] -> A[0, ...]
but given that we do have that rule, then A[None] -> A[None, ...] does make sense.
-n