On Jun 22, 2010, at 7:11 PM, David wrote:
>> Is it better to avoid setuptools/distribute/PyPI altogether?
>
> Yes, unless you need their features (which in the case of numpy is
> mostly egg, since installing from pypi rarely works anyway).
OK, installing from source solved the problem (and so di
On Mon, Jun 14, 2010 at 2:05 AM, David Goldsmith wrote:
> Hi, all! The scipy doc marathon has gotten off to a very slow start this
> summer. We are producing less than 1000 words a week, perhaps because
> many universities are still finishing up spring classes. So, this is
> a second appeal to
On 06/23/2010 09:38 AM, Geoffrey Ely wrote:
>
> Not sure if Python itself is available through PyPI/distribute. I installed
> Python 2.6.5 from source.
>
> As I understand it, setuptools does not work well for Numpy install, but
> distribute is a bit better. Is that true?
No, it is even worse.
On Tue, Jun 22, 2010 at 6:38 PM, Geoffrey Ely wrote:
> On Jun 22, 2010, at 5:13 PM, Benjamin Root wrote:
> > Which distro of Linux are you using? The kernel version is fairly old,
> but the installation date is less than a year old. Also, what version of
> python is available through Distribute
On Jun 22, 2010, at 5:13 PM, Benjamin Root wrote:
> Which distro of Linux are you using? The kernel version is fairly old, but
> the installation date is less than a year old. Also, what version of python
> is available through Distribute?
It is CentOS, heavily customized, I am sure, for this
On Tue, Jun 22, 2010 at 7:01 PM, Geoffrey Ely wrote:
> > No, and I cannot replicate it on OS X. Can you give more details about
> > your platform and how you built numpy?
>
> $ uname -a
> Linux login3.ranger.tacc.utexas.edu 2.6.9-78.0.22.EL_lustre_TACC #9 SMP
> Wed Nov 4 16:21:54 CST 2009 x86_64
> No, and I cannot replicate it on OS X. Can you give more details about
> your platform and how you built numpy?
$ uname -a
Linux login3.ranger.tacc.utexas.edu 2.6.9-78.0.22.EL_lustre_TACC #9 SMP
Wed Nov 4 16:21:54 CST 2009 x86_64 x86_64 x86_64 GNU/Linux
Python 2.6.5 built with
./configure
make
Hi Geoff,
It's working in numpy V1.3.0:
>>> import numpy
>>> numpy.__version__
'1.3.0'
>>> a = numpy.ones((1,1))
>>> a[a>0]
array([ 1.])
>>>
Thanks,
Masha
liu...@usc.edu
On Jun 22, 2010, at 4:37 PM, Geoffrey Ely wrote:
> Hi,
>
> I'm getting a SEGV for boolean indices
On Tue, Jun 22, 2010 at 18:37, Geoffrey Ely wrote:
> Hi,
>
> I'm getting a SEGV for boolean indices to a multi-dimensional array (numpy
> ver 1.4.1). Is this a known problem? Code and backtrace below.
No, and I cannot replicate it on OS X. Can you give more details about
your platform and how yo
Hi,
I'm getting a SEGV for boolean indices to a multi-dimensional array (numpy ver
1.4.1). Is this a known problem? Code and backtrace below.
Thanks,
Geoff
import numpy
a = numpy.ones((1,1))
a[a>0]
Program received signal SIGSEGV, Segmentation fault.
[Switching to Thread 182902359776 (LWP 17
On Tue, Jun 22, 2010 at 10:09 AM, Tom Durrant wrote:
>>
>> the basic idea is in "polyfit on multiple data points" on
>> numpy-disscusion mailing list April 2009
>>
>> In this case, calculations have to be done by groups
>>
>> subtract mean (this needs to be replaced by group demeaning)
>> modeldm
On 06/22/2010 09:13 AM, Tom Durrant wrote:
>
What exactly are trying to fit because it is rather bad practice
to fit
a model to some summarized data as you lose the uncertainty in the
original data?
If you define your boxes, you can loop through directly on each
box
>
>
> >
> What exactly are trying to fit because it is rather bad practice to fit
> a model to some summarized data as you lose the uncertainty in the
> original data?
> If you define your boxes, you can loop through directly on each box and
> even fit the equation:
>
> model=mu +beta1*obs
>
> The
>
>
> the basic idea is in "polyfit on multiple data points" on
> numpy-disscusion mailing list April 2009
>
> In this case, calculations have to be done by groups
>
> subtract mean (this needs to be replaced by group demeaning)
> modeldm = model - model.mean()
> obsdm = obs - obs.mean()
>
> xx =
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