[Numpy-discussion] Ticket #794 and can o' worms.
Charles R Harris
charlesr.harris@gmail....
Sun Jul 20 23:41:09 CDT 2008
On Sun, Jul 20, 2008 at 8:32 PM, Timothy Hochberg <tim.hochberg@ieee.org>
wrote:
>
>
> On Sun, Jul 20, 2008 at 3:47 PM, Robert Kern <robert.kern@gmail.com>
> wrote:
>
>> On Sun, Jul 20, 2008 at 17:42, Charles R Harris
>> <charlesr.harris@gmail.com> wrote:
>> > Hi All,
>> >
>> > I "fixed" ticket #754, but it leads to a ton of problems. The original
>> > discussion is here. The problems that arise come from conversion to
>> > different types.
>> >
>> > In [26]: a
>> > Out[26]: array([ Inf, -Inf, NaN, 0., 3., -3.])
>> >
>> > In [27]: sign(a).astype(int)
>> > Out[27]:
>> > array([ 1, -1, -2147483648, 0, 1,
>> > -1])
>> >
>> > In [28]: sign(a).astype(bool)
>> > Out[28]: array([ True, True, True, False, True, True], dtype=bool)
>> >
>> > In [29]: sign(a)
>> > Out[29]: array([ 1., -1., NaN, 0., 1., -1.])
>> >
>> > In [30]: bool(NaN)
>> > Out[30]: True
>> >
>> > So there are problems with at minimum the following.
>> >
>> > 1) The way NaN is converted to bool. I think it should be False.
>>
>> It's not really our choice. That's Python's bool(). For the things
>> that are our choice (e.g. array([nan]).astype(bool)) I think we should
>> stay consistent with Python.
>>
>
> <DELURK>
>
> I agree that this is a good goal. However, in the past, Python's treatment
> of NaNs has been rather platform dependent and add hock. In this case, I
> suspect that you are OK since the section "Truth Value Testing" in the
> Python docs is pretty clear that any non-zero value of a numerical type is
> True.
>
> However...
>
>
>>
>> > 2) The way NaN is converted to int types. I think it should be 0.
>>
>> I agree. That's what int(nan) gives:
>>
>> >>> int(nan)
>> 0L
>
>
>
> This is GvR in
> http://mail.python.org/pipermail/python-dev/2008-January/075865.html:
>
Well, now, that opens a whole other bag of toasted scorpions.
It looks like long(inf) and int(inf) already raise OverflowError and
> that should stay.
>
In [3]: (ones(2)*float(inf)).astype(int8)
Out[3]: array([0, 0], dtype=int8)
In [4]: (ones(2)*float(inf)).astype(int32)
Out[4]: array([-2147483648, -2147483648])
In [5]: (ones(2)*float(inf)).astype(int64)
Out[5]: array([-9223372036854775808, -9223372036854775808], dtype=int64)
Hmmm,
Chuck
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