Anne Archibald wrote:
> 2008/5/9 Eric Firing <[EMAIL PROTECTED]>:
>
>
>> It seems like some strategic re-thinking may be needed in the long run,
>> if not immediately.
> I think in principle the right answer is to simply run whatever
> underlying function, and mask any NaNs or Infs in the outpu
2008/5/9 Eric Firing <[EMAIL PROTECTED]>:
> It seems like some strategic re-thinking may be needed in the long run,
> if not immediately. There is a wide range of combinations of arguments
> that will trigger invalid results, whether Inf or NaN. The only way to
> trap and mask all of these is to
Pierre GM wrote:
> On Friday 09 May 2008 18:45:33 Eric Firing wrote:
>> I don't think the .max() part of that is right; the test needs to be
>> element-wise, and turned into a mask.
>
> Quite right. I was being overzealous...
>
>> It is also not clear to me that the test would actually catch all
On Friday 09 May 2008 18:45:33 Eric Firing wrote:
> I don't think the .max() part of that is right; the test needs to be
> element-wise, and turned into a mask.
Quite right. I was being overzealous...
> It is also not clear to me that the test would actually catch all the
> cases where x**b would
Pierre GM wrote:
> On Friday 09 May 2008 17:13:02 Eric Firing wrote:
>> Anne Archibald wrote:
>>> 2008/5/9 Eric Firing <[EMAIL PROTECTED]>:
md = make_mask((fb != fb.astype(int)) & (fa < 0), shrink=True)
>>> Unfortunately this isn't quite the right condition:
>>>
>>> In [18]: x = 2.**35; n
On Friday 09 May 2008 17:13:02 Eric Firing wrote:
> Anne Archibald wrote:
> > 2008/5/9 Eric Firing <[EMAIL PROTECTED]>:
> >> md = make_mask((fb != fb.astype(int)) & (fa < 0), shrink=True)
> >
> > Unfortunately this isn't quite the right condition:
> >
> > In [18]: x = 2.**35; numpy.array([-1.]
