Hello,
I would like to update my numpydoc so it works with sphinx 1.0, but I am not
sure where the dev version is; can someone point me in the right direction?
John
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On Tue, Aug 31, 2010 at 4:52 PM, Dan Elliott wrote:
> David Warde-Farley cs.toronto.edu> writes:
> > On 2010-08-30, at 10:36 PM, Charles R Harris wrote:
> > I think he means that if he needs both the determinant and to solve the
> > system, it might be more efficient to do
> > the SVD, obtain the
David Warde-Farley cs.toronto.edu> writes:
> On 2010-08-30, at 10:36 PM, Charles R Harris wrote:
> I think he means that if he needs both the determinant and to solve the
> system, it might be more efficient to do
> the SVD, obtain the determinant from the diagonal values, and obtain the
> solutio
Hi Chuck (and anyone else interested),
I updated the refactoring page on the NumPy developer wiki (seems to be down
or I'd paste in the link). It certainly isn't complete, but there are a lot
more details about the data structures and memory handling and an outline of
some additional topics that
On 2010-08-30, at 10:19 PM, Dan Elliott wrote:
> You don't think this will choke on a large (e.g. 10K x 10K) covariance
> matrix?
That depends. Is it very close to being rank deficient?That would be my main
concern. NumPy/LAPACK will have no trouble Cholesky-decomposing a matrix this
big, pr
31/08/10 @ 09:44 (-0700), thus spake Keith Goodman:
> 2010/8/31 Ernest Adrogué :
> > Hi,
> >
> > I find this a bit odd:
> >
> > In [18]: np.array(['a','b','c','d']) > 'a'
> > Out[18]: array([False, True, True, True], dtype=bool)
> >
> > In [19]: np.array(['a','b','c','d']) > 4
> > Out[19]: True
Hi Melissa,
On 30 August 2010 17:42, Melissa Mendonça wrote:
> I've been lurking for a while here but never really introduced myself.
> I'm a mathematician in Brazil working with optimization and numerical
> analysis and I'm looking into scipy/numpy basically because I want to
> ditch matlab.
W
2010/8/27 Brett Olsen :
> If there's multiple possible valid values, I've come up with a couple
> possible methods, but they all seem to be inefficient or kludges:
valid = N.array(("a", "c"))
(ar == valid[0]) | (ar == valid[1])
> array([ True, False, True, False, False, True, False, Tr
On 2010-08-30, at 10:36 PM, Charles R Harris wrote:
> I don't see what the connection with the determinant is. The log determinant
> will be calculated using the ordinary LU decomposition as that works for more
> general matrices.
I think he means that if he needs both the determinant and to so
Tue, 31 Aug 2010 12:03:55 -0500, Bruce Southey wrote:
[clip]
> I do understand that np.intp is integer size of a pointer. But it
> appears to be mainly used for access to C programs. The only Python
> numpy usage I saw was with the delete and insert function in
> 'numpy/lib/function_base.py'.
>
>
Hi,
I was curious why there was a difference in number of known failures
between Python2.6 and Python3.1 which is associated a test due to ticket 99:
http://projects.scipy.org/numpy/ticket/99
While this ticket was closed, it fails with Python 3.1 as indicated by
the message of the test output
On Tue, Aug 31, 2010 at 10:18 AM, Ralf Gommers
wrote:
> I am pleased to announce the availability of NumPy 1.5.0. This is the first
> NumPy release to include support for Python 3, as well as for Python 2.7.
>
> Binaries, sources, documentation and release notes can be found at
> https://sourcefor
2010/8/31 Ernest Adrogué :
> Hi,
>
> I find this a bit odd:
>
> In [18]: np.array(['a','b','c','d']) > 'a'
> Out[18]: array([False, True, True, True], dtype=bool)
>
> In [19]: np.array(['a','b','c','d']) > 4
> Out[19]: True
>
> In [20]: np.array(['a','b','c','d']) > 4.5
> Out[20]: True
>
> Is th
Hi,
I find this a bit odd:
In [18]: np.array(['a','b','c','d']) > 'a'
Out[18]: array([False, True, True, True], dtype=bool)
In [19]: np.array(['a','b','c','d']) > 4
Out[19]: True
In [20]: np.array(['a','b','c','d']) > 4.5
Out[20]: True
Is that right? I was expecting an element-wise comparis
I am pleased to announce the availability of NumPy 1.5.0. This is the first
NumPy release to include support for Python 3, as well as for Python 2.7.
Binaries, sources, documentation and release notes can be found at
https://sourceforge.net/projects/numpy/files/.
Thank you to everyone who contrib
On Tue, Aug 31, 2010 at 10:13 PM, David Huard wrote:
>
>
> On Tue, Aug 31, 2010 at 7:02 AM, Ralf Gommers > wrote:
>
>>
>>
>> On Tue, Aug 31, 2010 at 3:44 AM, David Huard wrote:
>>
>>>
>>> I just added a warning alerting concerned users (r8674), so this takes
>>> care of the bug fix and Nils wish
On Tue, Aug 31, 2010 at 7:02 AM, Ralf Gommers
wrote:
>
>
> On Tue, Aug 31, 2010 at 3:44 AM, David Huard wrote:
>
>>
>> I just added a warning alerting concerned users (r8674), so this takes
>> care of the bug fix and Nils wish to avoid a silent change in behavior.
>> These two changes could be inc
On Tue, Aug 31, 2010 at 3:57 AM, Mark Bakker wrote:
> Hello list,
>
> What is the easiest way to convert a function argument to at least a 1D
> array of specified dtype?
>
> atleast_1d(3,dtype='d') doesn't work (numpy 1.3.0)
>
> array(atleast_1d(3),dtype='d') works but seems cumbersome
atleast_1d
On Tue, Aug 31, 2010 at 3:44 AM, David Huard wrote:
>
> I just added a warning alerting concerned users (r8674), so this takes care
> of the bug fix and Nils wish to avoid a silent change in behavior. These two
> changes could be included in 1.5 if Ralf feels this is worthwhile.
>
> That looks li
Nathaniel Smith pobox.com> writes:
> On Fri, Aug 27, 2010 at 1:35 PM, Robert Kern gmail.com>
wrote:
> > As valid gets larger, in1d() will catch up but for smallish sizes of
> > valid, which I suspect given the "non-numeric" nature of the OP's (Hi,
> > Brett!) request, kern_in() is usually bette
Hello list,
What is the easiest way to convert a function argument to at least a 1D
array of specified dtype?
atleast_1d(3,dtype='d') doesn't work (numpy 1.3.0)
array(atleast_1d(3),dtype='d') works but seems cumbersome
Thanks,
Mark
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