On 11/28/06, Robert Kern <[EMAIL PROTECTED]> wrote:
Matthew Brett wrote:
> Hi,
>
> Sorry to keep cluttering the list, but I was a bit surprised by this
> behavior of allclose:
>
> In [25]:allclose([1.0], [1.0], rtol=0)
> Out[25]:True
>
> In [26]:allclose([1.0], [1.0], rtol=0, atol=0)
> Out[26]:F
Matthew Brett wrote:
> Hi,
>
> Sorry to keep cluttering the list, but I was a bit surprised by this
> behavior of allclose:
>
> In [25]:allclose([1.0], [1.0], rtol=0)
> Out[25]:True
>
> In [26]:allclose([1.0], [1.0], rtol=0, atol=0)
> Out[26]:False
>
> The docstring seems to imply that atol wil
Hi,
Sorry to keep cluttering the list, but I was a bit surprised by this
behavior of allclose:
In [25]:allclose([1.0], [1.0], rtol=0)
Out[25]:True
In [26]:allclose([1.0], [1.0], rtol=0, atol=0)
Out[26]:False
The docstring seems to imply that atol will not be used in this
comparison - or did I m
Matthew Brett wrote:
>Hi,
>
>Would it be easy and / or sensible for - say - int32.itemsize to
>return the same as dtype(int32).itemsize, rather than the 'itemsize' of 'numpy.generic' objects> that it returns at the moment?
>
>
>
You can do int32(0).itemsize.
Getting the itemsize attribute to "d
[EMAIL PROTECTED] wrote:
>On 24.11.2006, at 19:04, [EMAIL PROTECTED] wrote:
>
>
>
>>Those who would like to test-drive ScientificPython with NumPy can do
>>so now: just download version 2.7.1 from
>>
>> http://sourcesup.cru.fr/
>>
>>
>
>Version 2.7.2 now, after the first bug fix: it wou
Zhang Sam wrote:
> Thanks for so many replies.
>
> In fact, I want to use several arrays to store the original data from
> a practical project. In every arrays, two or three column will be
> store the index. The main computation is still on matrices(float type)
> which is built from the origi
Matthew Brett wrote:
>Hi,
>
>I was a bit confused by this on 32 bit linux:
>
>In [30]:sctypes['int']
>Out[30]:
>[,
> ,
> ,
> ,
> ]
>
>Is it easy to explain the two entries for int32 here? I notice there
>is only one int32 entry for the same test on my 64 bit system.
>
>
>
The mapping from c-typ
Hi,
Would it be easy and / or sensible for - say - int32.itemsize to
return the same as dtype(int32).itemsize, rather than the that it returns at the moment?
Best,
Matthew
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Matthew Brett wrote:
> Hi,
>
> I was a bit confused by this on 32 bit linux:
>
> In [30]:sctypes['int']
> Out[30]:
> [,
> ,
> ,
> ,
> ]
>
> Is it easy to explain the two entries for int32 here? I notice there
> is only one int32 entry for the same test on my 64 bit system.
Hmm. When we con
Hi,
I was a bit confused by this on 32 bit linux:
In [30]:sctypes['int']
Out[30]:
[,
,
,
,
]
Is it easy to explain the two entries for int32 here? I notice there
is only one int32 entry for the same test on my 64 bit system.
Thanks a lot,
Matthew
__
or if you want to stack array with more dimensions
concatenate((a[...,newaxis],b[...,newaxis]),axis=-1)
On 11/28/06, Robert Kern <[EMAIL PROTECTED]> wrote:
> Robert wrote:
> > I often need a function to zip 2 (1D) arrays together to a 2D array -
> > similar as python's zip() does.
> > Found no
Robert wrote:
> I often need a function to zip 2 (1D) arrays together to a 2D array - similar
> as python's zip() does.
> Found no function in numpy to do that directly without thinking a lot. Or is
> there one?
> Otherwise such thing would be helpful in future numpy.
column_stack()
--
Robert
Perfect, thanks!
Martin
On Tuesday 28 November 2006 17:31, Stefan van der Walt wrote:
> On Tue, Nov 28, 2006 at 04:59:22PM +0100, Martin Wiechert wrote:
> > I'm looking for an idiom to delete a sparse subset S (given as integer
> > indices into A) of elements from a 1d array A.
> >
> > Something
On Tue, Nov 28, 2006 at 04:59:22PM +0100, Martin Wiechert wrote:
> I'm looking for an idiom to delete a sparse subset S (given as integer
> indices
> into A) of elements from a 1d array A.
>
> Something like
>
> del L [I]
>
> if L is a list and I is a non-fancy index.
>
> My best idea is
>
>
Hi list,
I'm looking for an idiom to delete a sparse subset S (given as integer indices
into A) of elements from a 1d array A.
Something like
del L [I]
if L is a list and I is a non-fancy index.
My best idea is
B = ones (A.shape, bool)
B [S] = False
A = A [B]
Is there a better idiom (briefe
Original Message
Subject: python-dev Summary for 2006-11-01 through 2006-11-15
Date: Mon, 27 Nov 2006 04:52:10 + (GMT)
From: [EMAIL PROTECTED]
Newsgroups: gmane.comp.python.general
python-dev Summary for 2006-11-01 through 2006-11-15
Numpy readers will likely be interested in this excerpt.
Colin W.
Original Message
Subject: python-dev Summary for 2006-10-16 through 2006-10-31
Date: Mon, 27 Nov 2006 04:47:23 + (GMT)
From: [EMAIL PROTECTED]
Reply-To: python-list@python.org
Newsgroups: gmane.comp.python.ann
I often need a function to zip 2 (1D) arrays together to a 2D array - similar
as python's zip() does.
Found no function in numpy to do that directly without thinking a lot. Or is
there one?
Otherwise such thing would be helpful in future numpy.
I have this in my tools:
def azip( *ll ):
re
Hello,
I was wondering if someone could explain the rationale for changing
.var() and .std() in release 1.0b1 from normalizing by n-1 (unbiased
estimate from sample) to normalizing by n (population)?
I have found the note that this change happened in the Release Notes
http://www.scipy.org
Hi all,
I'm interested by an array where I can call rows by their names and play with
records, and recarray seems to be the solution. However, I can't find any
complete documentation saying if recarray have the following behaviors:
- stack datas : like numpy.array, is it possible to mak vstack,
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