Hi all,
Due to changes in our firewall configuration, the buildbot was offline
since 14 January. Everything is sorted out now, so you can access the
buildbot again at
http://buildbot.scipy.org
Some of the build slaves are still offline, and I have contacted their
maintainers.
Regards
Stéfan
__
> I have tried with linalg.solve(a, b), where I put a as the
> Matrix A - eigen value* unit matrix, and b as the zero matrix. But the
> solution returned is a zero matrix, which I really find disappointing.
So if you're trying to solve (A - \lambda I) x = b, try appending an
extra row to your
Hey all,
I am currently working on a large matrix, and I already have a
specific eigen value that I want to use in order to find out its
corresponding eigen vector. Is there an easy way to do so?
I have tried with linalg.solve(a, b), where I put a as the
Matrix A - eigen value* unit mat
Ondrej Certik wrote:
>> That's exactly what I don't like about cmake - it means you can't
>> produce accurate builds (you need to rerun cmake everytime you change
>> the configuration or dependencies, whereas this is automatic with
>> scons/waf). It also have (used to have) very poor documentation
> That's exactly what I don't like about cmake - it means you can't
> produce accurate builds (you need to rerun cmake everytime you change
> the configuration or dependencies, whereas this is automatic with
> scons/waf). It also have (used to have) very poor documentation unless
> you buy the boo
On Mon, Feb 9, 2009 at 5:17 AM, Brian Granger wrote:
>> I don't find it that surprising - numpy and scipy require some
>> relatively advanced features (mixed language and cross-platform with
>> support for many toolchains). Within the open source tools, I know
>> only two which can handle those re
On Mon, Feb 9, 2009 at 4:39 AM, Ondrej Certik wrote:
>
> Yes, I am investigating cmake, it's pretty cool. I wrote some macros
> for cython etc. What I like about cmake is that it is cross platform
> and it just produces makefiles on linux, or visual studio files (or
> whatever) on windows. When
On Sun, Feb 8, 2009 at 12:26 PM, Brian Granger wrote:
>> Yes, I am investigating cmake, it's pretty cool. I wrote some macros
>> for cython etc. What I like about cmake is that it is cross platform
>> and it just produces makefiles on linux, or visual studio files (or
>> whatever) on windows. Whe
> Yes, I am investigating cmake, it's pretty cool. I wrote some macros
> for cython etc. What I like about cmake is that it is cross platform
> and it just produces makefiles on linux, or visual studio files (or
> whatever) on windows. When I get more experience with it, I'll post
> here.
Yes, wh
> I don't find it that surprising - numpy and scipy require some
> relatively advanced features (mixed language and cross-platform with
> support for many toolchains). Within the open source tools, I know
> only two which can handle those requirements: scons and cmake. For
> example, it would almos
On Sun, Feb 8, 2009 at 3:10 AM, David Cournapeau wrote:
> On Sun, Feb 8, 2009 at 3:21 AM, Ondrej Certik wrote:
>> Hi David,
>>
>>> Sorry for the confusion: numscons is NOT the preferred build system.
>>> The current numpy.distutils extensions, as shipped by numpy, is the
>>> preferred one. Numsco
I am seeing some really strange behavior when I try to pass an ndarray
subclass and out=None to numpy's ufuncs. This example will reproduce the
problem with svn numpy, the first print statement yields 1 as expected, the
second yields "" and the third yields a
segmentation fault:
import numpy as n
Francesc Alted pytables.org> writes:
> > What are some common use cases for this feature?
> >
> > I use structured arrays quite a lot, but I haven't found myself
> > wanting something like this. If I do need a subset of a structured
> > array generally I use something like
> >
> > [rec[n] for n i
On Sun, Feb 8, 2009 at 8:02 AM, wrote:
> But I just found that nan in the exponent in an array are not propagated:
>
0.0 ** np.array([-np.nan, 0, 1], dtype=np.int32)[0]
> 1.0
np.power(0.0, np.array([-np.nan, 0, 1], dtype=np.int32)[0])
> 1.0
correction:
np.power propagates nans, it's a
On Sun, Feb 8, 2009 at 5:09 AM, Robert Kern wrote:
> On Sun, Feb 8, 2009 at 03:54, Stéfan van der Walt wrote:
>> Hi all,
>>
>> Ticket #955 (http://scipy.org/scipy/numpy/ticket/955) touches on the
>> following issue:
>>
> 0.0 ** np.array([-1, 0, 1], dtype=np.int32)
>> array([ Inf, 1., 0.])
On Sun, Feb 8, 2009 at 3:21 AM, Ondrej Certik wrote:
> Hi David,
>
>> Sorry for the confusion: numscons is NOT the preferred build system.
>> The current numpy.distutils extensions, as shipped by numpy, is the
>> preferred one. Numscons is more an experiment, if you want.
>
> Ah, I see, thanks for
On Sun, Feb 8, 2009 at 03:54, Stéfan van der Walt wrote:
> Hi all,
>
> Ticket #955 (http://scipy.org/scipy/numpy/ticket/955) touches on the
> following issue:
>
0.0 ** np.array([-1, 0, 1], dtype=np.int32)
> array([ Inf, 1., 0.])
0.0 ** np.array([-1, 0, 1], dtype=np.int32)[0]
> --
Hi all,
Ticket #955 (http://scipy.org/scipy/numpy/ticket/955) touches on the
following issue:
>>> 0.0 ** np.array([-1, 0, 1], dtype=np.int32)
array([ Inf, 1., 0.])
>>> 0.0 ** np.array([-1, 0, 1], dtype=np.int32)[0]
Traceback (most r
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