On Wed, Dec 31, 2008 at 11:28 AM, Pauli Virtanen wrote:
> Mon, 29 Dec 2008 13:38:12 +0900, David Cournapeau wrote:
> [clip]
>> I put my yesterday work in the fix_float_format branch:
>> - it fixes the locale issue
>> - it fixes the long double issue on windows.
>> - it also fixes some tests (we
On Wed, Dec 31, 2008 at 3:41 AM, Lenard Lindstrom wrote:
> David Cournapeau wrote:
>> The thing is since I don't understand what happens in the print '%f'
>> case, I don't know how to clean it up, if it is at all possible. But in
>> anyway, it means that with my changes, we are not worse than pyth
Mon, 29 Dec 2008 13:38:12 +0900, David Cournapeau wrote:
[clip]
> I put my yesterday work in the fix_float_format branch:
> - it fixes the locale issue
> - it fixes the long double issue on windows.
> - it also fixes some tests (we were not testing single precision
>formatting but twice dou
Tue, 30 Dec 2008 14:17:30 -0800, Christopher Barker wrote:
> Robert Kern wrote:
>> Well, True and False are singletons,
>
> I thought so.
>
>> but numpy.any() and numpy.all()
>> don't return bools. They return numpy.bool_s.
>
> Is that a numpy scalar type?
>
> This also begs the question: why
Robert Kern wrote:
> Well, True and False are singletons,
I thought so.
> but numpy.any() and numpy.all()
> don't return bools. They return numpy.bool_s.
Is that a numpy scalar type?
This also begs the question: why don't they return regular old True and
False?
-CHB
--
Christopher Barker
On Tue, Dec 30, 2008 at 14:59, Christopher Barker wrote:
> Hi all,
>
> I've just discovered that "False" is not a singleton:
>
> >>> import numpy as N
>
> >>> f = N.all((1,2,0))
> >>> print f
> False
> >>> id(f)
> 17316364
> >>> f is False
> False
> >>> id(False)
> 3294768
>
>
> Should it be
On Tue, Dec 30, 2008 at 2:59 PM, Christopher Barker
wrote:
> Hi all,
>
> I've just discovered that "False" is not a singleton:
>
> >>> import numpy as N
>
> >>> f = N.all((1,2,0))
> >>> print f
> False
> >>> id(f)
> 17316364
> >>> f is False
> False
> >>> id(False)
> 3294768
>
>
> Should it
Hi all,
I've just discovered that "False" is not a singleton:
>>> import numpy as N
>>> f = N.all((1,2,0))
>>> print f
False
>>> id(f)
17316364
>>> f is False
False
>>> id(False)
3294768
Should it be?
This arose for me in some tests I'm using that check if a result is False:
self.fail
David Cournapeau wrote:
> The thing is since I don't understand what happens in the print '%f'
> case, I don't know how to clean it up, if it is at all possible. But in
> anyway, it means that with my changes, we are not worse than python
> itself, and I think we are better than before,
>
>
Just
On Tue, 30 Dec 2008, Nicholas Matsakis wrote:
> I just installed what I believe to be a completely vanilla installation of
> numpy on an Intel Mac OS X 10.5.6. Python 2.5 pkg from Python.org, numpy
> 1.2.1 pkg from scipy.org, nose installed through setup tools. Running
> "import numpy; numpy.te
On Tue, Dec 30, 2008 at 10:10 AM, ctw wrote:
> Hi!
>
> I'm a bit stumped by the following: suppose I have several recarrays
> with identical dtypes (identical field names, etc.) and would like to
> combine them into one rec array, what would be the best way to do
> that? I tried using np.rec.fromr
Hi!
I'm a bit stumped by the following: suppose I have several recarrays
with identical dtypes (identical field names, etc.) and would like to
combine them into one rec array, what would be the best way to do
that? I tried using np.rec.fromrecords, but that doesn't produce the
desired result. As a
I just installed what I believe to be a completely vanilla installation of
numpy on an Intel Mac OS X 10.5.6. Python 2.5 pkg from Python.org, numpy
1.2.1 pkg from scipy.org, nose installed through setup tools. Running
"import numpy; numpy.test()" results in the following errors and failures:
A Tuesday 30 December 2008, Francesc Alted escrigué:
> A Monday 29 December 2008, Jean-Baptiste Rudant escrigué:
[snip]
>
> The difference for both approaches is that the row-wise arrangement
> is more efficient when data is iterated by field, while the
> column-wise one is more efficient when data
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