On Sun, Jul 20, 2008 at 8:32 PM, Timothy Hochberg <[EMAIL PROTECTED]> wrote:
> > > On Sun, Jul 20, 2008 at 3:47 PM, Robert Kern <[EMAIL PROTECTED]> > wrote: > >> On Sun, Jul 20, 2008 at 17:42, Charles R Harris >> <[EMAIL PROTECTED]> 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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