On Di, 2014-02-25 at 17:52 -0500, Scott Ransom wrote:
> Hi All,
>
> So I have a need to use longdouble numpy scalars in an application, and
> I need to be able to reliably set long-double precision values in them.
> Currently I don't see an easy way to do that. For example:
>
> In [19]: numpy.longdouble("1.12345678901234567890")
> Out[19]: 1.1234567890123456912
>
> Note the loss of those last couple digits.
>
> In [20]: numpy.float("1.12345678901234567890")
> Out[20]: 1.1234567890123457
>
> In [21]: numpy.longdouble("1.12345678901234567890") -
> numpy.float("1.12345678901234567890")
> Out[21]: 0.0
>
> And so internally they are identical.
>
> In this case, the string appears to be converted to a C double (i.e.
> numpy float) before being assigned to the numpy scalar. And therefore
> it loses precision.
>
> Is there a good way of setting longdouble values? Is this a numpy bug?
>
Yes, this is a bug I think (never checked), we use the python parsing
functions where possible. But for longdouble python float (double) is
obviously not enough. A hack would be to split it into two:
np.float128(1.1234567890) + np.float128(1234567890e-something)
Though it would be better for the numpy parser to parse the full
precision when given a string.
- Sebastian
> I was considering using a tiny cython wrapper of strtold() to do a
> conversion from a string to a long double, but it seems like this is
> basically what should be happening internally in numpy in the above example!
>
> Thanks,
>
> Scott
>
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