> If we want to design ndrange with the intent of indexing only This is the only use I had in mind. But I feel like you are able to envision different use cases.
> Whatever we pick, the return value should be such that > np.array(ndrange(...))[ind] == ndrange(...)[idx] I can see the appeal to this. On Thu, Oct 11, 2018 at 10:31 PM Eric Wieser <wieser.eric+nu...@gmail.com> wrote: > I’m not sure if we ever want the ndrange object to return a full matrix. > > np.array(ndrange(...)) should definitely return a full array, because > that’s what the user asked for. > > Even if you supply a numpy uint8 to range, it still returns a python int > class. > > If we want to design ndrange with the intent of indexing only, then it > should probably always use np.intp, whatever the type of the provided > arguments > > Would you like ndrange to return a tuple of uint8 in this case? > > Tuples are just one of the four options I listed in a previous message. > The downside of tuples is there’s no easy way to say “take just the first > axis of this range”. > Whatever we pick, the return value should be such that > np.array(ndrange(...))[ind] > == ndrange(...)[idx] > > On Thu, 11 Oct 2018 at 18:54 Mark Harfouche <mark.harfou...@gmail.com> > wrote: > >> Eric, interesting ideas. >> >> > __getitem__(Tuple[int]) which returns numpy scalars >> >> I'm not sure what you mean. Even if you supply a numpy uint8 to range, it >> still returns a python int class. >> Would you like ndrange to return a tuple of `uint8` in this case? >> >> ``` >> In [3]: a = >> iter(range(np.uint8(10))) >> >> In [4]: >> next(a).__class__ >> Out[4]: int >> >> In [5]: >> np.uint8(10).__class__ >> Out[5]: numpy.uint8 >> ``` >> >> Ravel seems like a cool way to choose iteration order. In the PR, I >> mentionned that one reason that I removed `'F'` order from the PR was: >> 1. My implementation was not competitive with the `C` order >> implementation in terms of speed (can be fixed) >> 2. I don't know if it something that people really need to iterate over >> collections (annoying to maintain if unused) >> >> Instead, I just showed an example how people could iterate in `F` order >> should they need to. >> >> I'm not sure if we ever want the `ndrange` object to return a full >> matrix. It seems like we would be creating a custom tuple class just for >> this which seems pretty niche. >> >> >> On Thu, Oct 11, 2018 at 10:21 AM Eric Wieser <wieser.eric+nu...@gmail.com> >> wrote: >> >>> Isn’t that what arange is for? >>> >>> Imagining ourselves in python2 land for now - I’m proposing arange is >>> to range, as ndrange is to xrange >>> >>> I’m not convinced it should return an ndarray >>> >>> I agree - I think it should return a range-like object that: >>> >>> - Is convertible via __array__ if needed >>> - Looks like an ndarray, with: >>> - a .dtype attribute >>> - a __getitem__(Tuple[int]) which returns numpy scalars >>> - .ravel() and .flat for choosing iteration order. >>> >>> On Wed, 10 Oct 2018 at 11:21 Allan Haldane allanhald...@gmail.com >>> <http://mailto:allanhald...@gmail.com> wrote: >>> >>> On 10/10/18 12:34 AM, Eric Wieser wrote: >>>> > One thing that worries me here - in python, |range(...)| in essence >>>> > generates a lazy |list| - so I’d expect |ndrange| to generate a lazy >>>> > |ndarray|. In practice, that means it would be a duck-type defining an >>>> > |__array__| method to evaluate it, and only implement methods already >>>> > present in numpy. >>>> >>>> Isn't that what arange is for? >>>> >>>> It seems like there are two uses of python3's range: 1. creating a 1d >>>> iterable of indices for use in for-loops, and 2. with list(range) can be >>>> used to create a sequence of integers. >>>> >>>> Numpy can extend this in two directions: >>>> * ndrange returns an iterable of nd indices (for for-loops). >>>> * arange returns an 1d ndarray of integers instead of a list >>>> >>>> The application of for-loops, which is more niche, doesn't need >>>> ndarray's vectorized properties, so I'm not convinced it should return >>>> an ndarray. It certainly seems simpler not to return an ndarray, due to >>>> the dtype question. >>>> >>>> arange on its own seems to cover the need for a vectorized version of >>>> range. >>>> >>>> Allan >>>> _______________________________________________ >>>> NumPy-Discussion mailing list >>>> NumPy-Discussion@python.org >>>> https://mail.python.org/mailman/listinfo/numpy-discussion >>>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@python.org >>> https://mail.python.org/mailman/listinfo/numpy-discussion >>> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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