On 9/13/19 7:23 AM, Andras Deak wrote:

I just want to add that you can use literal 16.055 to reproduce this:

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import numpy as np np.set_printoptions(precision=2) np.array([16.055]).round(2) array([16.06]) np.array([16.055]) array([16.05])

I would think it has to do with "round to nearest even":

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np.array(16.055) array(16.05) np.array(16.065) array(16.07) np.array(16.065).round(2) 16.07

But it's as if `round` rounded decimal digits upwards (16.055 -> 16.06, 16.065 -> 16.07), whereas the `repr` rounded to the nearest odd(!) digit (16.055 -> 16.05, 16.065 -> 16.07). Does this make any sense? I'm on numpy 1.17.2. (Scalars or 1-length 1d arrays don't seem to make a difference). Regards,

András

Isn't that just for consistency with Python 3 round()?  I agree that the discrepancy with np.set_printoptions is not necessarily expected, except possibly for backwards compatibility. Phil

On 9/13/19 8:45 AM, Irvin Probst wrote:

On 13/09/2019 14:05, Philip Hodge wrote:

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Isn't that just for consistency with Python 3 round()?  I agree that the discrepancy with np.set_printoptions is not necessarily expected, except possibly for backwards compatibility.

I've just checked and np.set_printoptions behaves as python's round:

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round(16.055,2) 16.05 np.round(16.055,2) 16.06

I don't know why round and np.round do not behave the same, actually I would even dare to say that I don't care :-) However np.round and np.set_printoptions should provide the same output, shouldn't they ? This discrepancy is really disturbing whereas consistency with python's round looks like the icing on the cake but in no way a required feature.

Python round() is supposed to round to the nearest even value, if the two closest values are equally close.  So round(16.055, 2) returning 16.05 was a surprise to me.  I checked the documentation and found a note that explained that this was because "most decimal fractions can't be represented exactly as a float."  round(16.55) returns 16.6. Phil

See the notes section here. https://numpy.org/devdocs/reference/generated/numpy.around.html. This note was recently added in https://github.com/numpy/numpy/pull/14392 Eric On Fri, Sep 13, 2019 at 9:20 AM Andras Deak wrote:

On Fri, Sep 13, 2019 at 2:59 PM Philip Hodge wrote:

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On 9/13/19 8:45 AM, Irvin Probst wrote:

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On 13/09/2019 14:05, Philip Hodge wrote:

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Isn't that just for consistency with Python 3 round()? I agree that the discrepancy with np.set_printoptions is not necessarily expected, except possibly for backwards compatibility.

I've just checked and np.set_printoptions behaves as python's round:

...

...

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round(16.055,2) 16.05 np.round(16.055,2) 16.06

I don't know why round and np.round do not behave the same, actually I would even dare to say that I don't care :-) However np.round and np.set_printoptions should provide the same output, shouldn't they ? This discrepancy is really disturbing whereas consistency with python's round looks like the icing on the cake but in no way a required feature.

Python round() is supposed to round to the nearest even value, if the two closest values are equally close. So round(16.055, 2) returning 16.05 was a surprise to me. I checked the documentation and found a note that explained that this was because "most decimal fractions can't be represented exactly as a float." round(16.55) returns 16.6.

Phil

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Ah, of course, endless double-precision shenanigans...

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format(16.055, '.30f') '16.054999999999999715782905695960'

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format(16.55, '.30f') '16.550000000000000710542735760100'

András _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion

On 9/13/19 9:26 AM, Eric Moore wrote:

See the notes section here.   https://numpy.org/devdocs/reference/generated/numpy.around.html.

This note was recently added in https://github.com/numpy/numpy/pull/14392

Eric

Hmm, but this example with 16.055 shows the note still isn't quite right. The doc suggests that the floating point error only matters for large values or large `decimals`, but this shows it also happens for small values. Makes sense now that I see the example. We should tweak the docstring. Also, I did make some notes in https://github.com/numpy/numpy/issues/14391 for how we could "fix" this problem efficiently. Unfortunately it's far from trivial to write a correct rounding algorithm, and I'm not sure it's worth the effort: The round error is comparable to normal floating-point error, and I don't think round is heavily used. Best, Allan

On Fri, Sep 13, 2019 at 9:20 AM Andras Deak mailto:deak.andris@gmail.com> wrote:

On Fri, Sep 13, 2019 at 2:59 PM Philip Hodge mailto:hodge@stsci.edu> wrote: > > On 9/13/19 8:45 AM, Irvin Probst wrote: > > On 13/09/2019 14:05, Philip Hodge wrote: > >> > >> Isn't that just for consistency with Python 3 round()?  I agree that > >> the discrepancy with np.set_printoptions is not necessarily expected, > >> except possibly for backwards compatibility. > >> > >> > > > > I've just checked and np.set_printoptions behaves as python's round: > > > > >>> round(16.055,2) > > 16.05 > > >>> np.round(16.055,2) > > 16.06 > > > > I don't know why round and np.round do not behave the same, actually I > > would even dare to say that I don't care :-) > > However np.round and np.set_printoptions should provide the same > > output, shouldn't they ? This discrepancy is really disturbing whereas > > consistency with python's round looks like the icing on the cake but > > in no way a required feature. > > > > Python round() is supposed to round to the nearest even value, if the > two closest values are equally close.  So round(16.055, 2) returning > 16.05 was a surprise to me.  I checked the documentation and found a > note that explained that this was because "most decimal fractions can't > be represented exactly as a float."  round(16.55) returns 16.6. > > Phil > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org mailto:NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion

Ah, of course, endless double-precision shenanigans... >>> format(16.055, '.30f') '16.054999999999999715782905695960'

>>> format(16.55, '.30f') '16.550000000000000710542735760100'

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