On Sat, Feb 22, 2020 at 9:34 AM <josef.p...@gmail.com> wrote: > not having a hashable tuple conversion would be a strong limitation > > a = tuple(np.arange(5)) > versus > a = tuple([np.array(i) for i in range(5)]) > {a:5} >
also there is the question of which scalar .item() versus [()] This was used in the old times in scipy.stats, and I just saw https://github.com/scipy/scipy/pull/11165#issuecomment-589952838 aside: AFAIR, I use 0-dim arrays also to ensure that I have a numpy dtype and not, e.g. some equivalent python type Josef > > Josef > > On Sat, Feb 22, 2020 at 9:28 AM Evgeni Burovski < > evgeny.burovs...@gmail.com> wrote: > >> Hi Sebastian, >> >> Just to clarify the difference: >> >> >>> x = np.float64(42) >> >>> y = np.array(42, dtype=float) >> >> Here `x` is a scalar and `y` is a 0D array, correct? >> If that's the case, not having the former would be very confusing for >> users (at least, that would be very confusing to me, FWIW). >> >> If anything, I think it'd be cleaner to not have the latter, and only >> have either scalars or 1D arrays (i.e., N-D arrays with N>=1), but it >> is probably way too late to even think about it anyway. >> >> Cheers, >> >> Evgeni >> >> On Sat, Feb 22, 2020 at 4:37 AM Sebastian Berg >> <sebast...@sipsolutions.net> wrote: >> > >> > Hi all, >> > >> > When we create new datatypes, we have the option to make new choices >> > for the new datatypes [0] (not the existing ones). >> > >> > The question is: Should every NumPy datatype have a scalar associated >> > and should operations like indexing return a scalar or a 0-D array? >> > >> > This is in my opinion a complex, almost philosophical, question, and we >> > do not have to settle anything for a long time. But, if we do not >> > decide a direction before we have many new datatypes the decision will >> > make itself... >> > So happy about any ideas, even if its just a gut feeling :). >> > >> > There are various points. I would like to mostly ignore the technical >> > ones, but I am listing them anyway here: >> > >> > * Scalars are faster (although that can be optimized likely) >> > >> > * Scalars have a lower memory footprint >> > >> > * The current implementation incurs a technical debt in NumPy. >> > (I do not think that is a general issue, though. We could >> > automatically create scalars for each new datatype probably.) >> > >> > Advantages of having no scalars: >> > >> > * No need to keep track of scalars to preserve them in ufuncs, or >> > libraries using `np.asarray`, do they need `np.asarray_or_scalar`? >> > (or decide they return always arrays, although ufuncs may not) >> > >> > * Seems simpler in many ways, you always know the output will be an >> > array if it has to do with NumPy. >> > >> > Advantages of having scalars: >> > >> > * Scalars are immutable and we are used to them from Python. >> > A 0-D array cannot be used as a dictionary key consistently [1]. >> > >> > I.e. without scalars as first class citizen `dict[arr1d[0]]` >> > cannot work, `dict[arr1d[0].item()]` may (if `.item()` is defined, >> > and e.g. `dict[arr1d[0].frozen()]` could make a copy to work. [2] >> > >> > * Object arrays as we have them now make sense, `arr1d[0]` can >> > reasonably return a Python object. I.e. arrays feel more like >> > container if you can take elements out easily. >> > >> > Could go both ways: >> > >> > * Scalar math `scalar = arr1d[0]; scalar += 1` modifies the array >> > without scalars. With scalars `arr1d[0, ...]` clarifies the >> > meaning. (In principle it is good to never use `arr2d[0]` to >> > get a 1D slice, probably more-so if scalars exist.) >> > >> > Note: array-scalars (the current NumPy scalars) are not useful in my >> > opinion [3]. A scalar should not be indexed or have a shape. I do not >> > believe in scalars pretending to be arrays. >> > >> > I personally tend towards liking scalars. If Python was a language >> > where the array (array-programming) concept was ingrained into the >> > language itself, I would lean the other way. But users are used to >> > scalars, and they "put" scalars into arrays. Array objects are in some >> > ways strange in Python, and I feel not having scalars detaches them >> > further. >> > >> > Having scalars, however also means we should preserve them. I feel in >> > principle that is actually fairly straight forward. E.g. for ufuncs: >> > >> > * np.add(scalar, scalar) -> scalar >> > * np.add.reduce(arr, axis=None) -> scalar >> > * np.add.reduce(arr, axis=1) -> array (even if arr is 1d) >> > * np.add.reduce(scalar, axis=()) -> array >> > >> > Of course libraries that do `np.asarray` would/could basically chose to >> > not preserve scalars: Their signature is defined as taking strictly >> > array input. >> > >> > Cheers, >> > >> > Sebastian >> > >> > >> > [0] At best this can be a vision to decide which way they may evolve. >> > >> > [1] E.g. PyTorch uses `hash(tensor) == id(tensor)` which is arguably >> > strange. E.g. Quantity defines hash correctly, but does not fully >> > ensure immutability for 0-D Quantities. Ensuring immutability in a >> > world where "views" are a central concept requires a write-only copy. >> > >> > [2] Arguably `.item()` would always return a scalar, but it would be a >> > second class citizen. (Although if it returns a scalar, at least we >> > already have a scalar implementation.) >> > >> > [3] They are necessary due to technical debt for NumPy datatypes >> > though. >> > _______________________________________________ >> > 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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