Pauli Virtanen wrote:
> We'll just need to add long double versions of NumPyOS_ascii_strtod and
> NumPyOS_ftolf that call sscanf with the correct format string in the end.
hmm -- if you're going to do that, maybe we could re-factor and use
sscanf everywhere instead of ato*() -- the ato* function
Thu, 02 Sep 2010 18:13:23 +0100, Colin Macdonald wrote:
> On 09/02/10 17:06, Christopher Barker wrote:
>> Does the clib for a compiler that provides a float64 also provide an
>> atof() function that supports it? Its seems that it should.
>
> I think so, for example in C I can do:
>
> fscanf(fp, "
On 09/02/10 17:06, Christopher Barker wrote:
> Does the clib for a compiler that provides a float64 also provide an
> atof() function that supports it? Its seems that it should.
I think so, for example in C I can do:
fscanf(fp, "%Lf %Lf %Lf", &x, &y, &z);
where x,y,z are "long doubles".
The equ
Charles R Harris wrote:
>>> So if you write "float96(0.0001)", the result is not the float96 number
>>> closest to 0.0001, but the 96-bit representation of the 64-bit number
>>> closest to 0.0001.
...
>> but wouldn't it be better to exactly handle strings since those can be
>> converted exactly, w
On Thu, Sep 2, 2010 at 10:03 AM, Charles R Harris
wrote:
>
>
> On Wed, Sep 1, 2010 at 3:30 PM, Michael Gilbert
> wrote:
>>
>> On Wed, 1 Sep 2010 21:15:22 + (UTC), Pauli Virtanen wrote:
>> > Wed, 01 Sep 2010 16:26:59 -0400, Michael Gilbert wrote:
>> > > I've been using numpy's float96 class la
On Wed, Sep 1, 2010 at 3:30 PM, Michael Gilbert wrote:
> On Wed, 1 Sep 2010 21:15:22 + (UTC), Pauli Virtanen wrote:
> > Wed, 01 Sep 2010 16:26:59 -0400, Michael Gilbert wrote:
> > > I've been using numpy's float96 class lately, and I've run into some
> > > strange precision errors.
> > [clip]
On 09/01/10 22:30, Michael Gilbert wrote:
> Interesting. float96( '0.0001' ) also seems to evaluate to the first
> result. I assume that it also does a float64( '0.0001' ) conversion
> first. I understand that you can't change how python passes in floats,
> but wouldn't it be better to exactly han
On Wed, 1 Sep 2010 21:15:22 + (UTC), Pauli Virtanen wrote:
> Wed, 01 Sep 2010 16:26:59 -0400, Michael Gilbert wrote:
> > I've been using numpy's float96 class lately, and I've run into some
> > strange precision errors.
> [clip]
> > >>> x = numpy.array( [0.01] , numpy.float96 )
> [clip]
> > I
Wed, 01 Sep 2010 16:26:59 -0400, Michael Gilbert wrote:
> I've been using numpy's float96 class lately, and I've run into some
> strange precision errors.
[clip]
> >>> x = numpy.array( [0.01] , numpy.float96 )
[clip]
> I would expect the float96 calculation to also produce 0.0 exactly as
> found
On Wed, Sep 1, 2010 at 2:26 PM, Michael Gilbert wrote:
> Hi,
>
> I've been using numpy's float96 class lately, and I've run into some
> strange precision errors. See example below:
>
> >>> import numpy
> >>> numpy.version.version
> '1.5.0'
> >>> sys.version
> '3.1.2 (release31-maint, Jul 8
Hi,
I've been using numpy's float96 class lately, and I've run into some
strange precision errors. See example below:
>>> import numpy
>>> numpy.version.version
'1.5.0'
>>> sys.version
'3.1.2 (release31-maint, Jul 8 2010, 01:16:48) \n[GCC 4.4.4]'
>>> x = numpy.array( [0.01] , numpy.
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