On Thu, 2022-09-08 at 23:19 -0700, Stefan van der Walt wrote:
I am in favor of such a change. It will make what is returned more transparent to users (and reduce confusion for newcomers).
With NEP50, we're already adopting a philosophy of explicit scalar usage anyway: no longer pretending or trying to make transparent that Python floats and NumPy floats are the same.
No one *actually* round-trips objects via repr, but if a user could look at a result and know how to construct the object, that is an improvement.
True, the only worry would be loss of a bit of a precision when someone has a copy-paste workflow. But at that point, users probably don't care about the last ULP. Even float32/float16 `repr` use is tricky in principle since float32("<number>") float32(<number>) May not be identical, but I would have to dig into the subtleties there (and presumably NumPy gets it subtly wrong anyway right now!). Not sure it is a big worry, but probably worthwhile to note down if we end up writing a brief NEP. Cheers, Sebastian
Stéfan
On Thu, Sep 8, 2022, at 22:26, Matti Picus wrote:
On 9/9/22 04:15, Warren Weckesser wrote:
... To quote from https://docs.python.org/3/library/functions.html#repr:
For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval(); Sebastian, is this an explicit goal of the change? (Personally, I've gotten used to not taking this too seriously, but my world view is biased by the long-term use of NumPy, which has never followed this guideline.)
If that is a goal, than the floating point types with precision greater than double precision will need to display the argument of the type as a string. For example, the following is run on a platform where numpy.longdouble is extended precision (80 bits):
``` In [161]: longpi = np.longdouble('3.14159265358979323846')
In [162]: longpi Out[162]: 3.1415926535897932385
In [163]: np.longdouble(3.1415926535897932385) # Argument is parsed as 64 bit float Out[163]: 3.141592653589793116
In [164]: np.longdouble('3.1415926535897932385') # Correctly reproduces the longdouble Out[164]: 3.1415926535897932385 ```
Warren
As others have mentioned, the change will greatly enhance UX at the cost of documentation cleanups. While the representation may not be perfectly roundtrip-able, I think it still is an improvement and worthwhile. Elsewhere I have suggested we need more documentation around array/scalar printing, perhaps that would be a place to mention the limitations of string representations.
Matti
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