Has anyone gotten the combination of OS X with a fink python
distribution to successfully build numpy/scipy with the intel
compilers and the mkl? If so, how'd you do it?
-gideon
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>> I'm curious about how to control compiler options for mingw builds of
>> numpy on windows... Specifically, I want to build binaries without
>> SSE
>> support, so that they can run on older hardware.
>
> The windows binaries of numpy can run on machines without SSE support.
> If for some reason
On Sat, Nov 29, 2008 at 5:35 AM, Zachary Pincus <[EMAIL PROTECTED]> wrote:
> Hi all,
>
> I'm curious about how to control compiler options for mingw builds of
> numpy on windows... Specifically, I want to build binaries without SSE
> support, so that they can run on older hardware.
The windows bin
Hi all,
I'm curious about how to control compiler options for mingw builds of
numpy on windows... Specifically, I want to build binaries without SSE
support, so that they can run on older hardware.
Setting a CFLAGS variable on the command-line doesn't appear to do
anything, but perhaps appe
Manuel,
Give me the week-end to come up with something. What you want is
already doable with the current implementation of np.loadtxt, through
the converter keyword. Support for missing data will be covered in a
separate function, most likely to be put in numpy.ma.io at term.
On Nov 28, 2
Pierre GM wrote:
> On Nov 27, 2008, at 3:08 AM, Manuel Metz wrote:
>> Certainly, yes! Dealing with fixed-length fields would be necessary.
>> The
>> case I had in mind had both -- a separator ("|") __and__ fixed-length
>> fields -- and is probably very special in that sense. But such
>> data-file
2008/11/28 T J <[EMAIL PROTECTED]>:
import numpy as np
x = np.ones((3,0))
x
> array([], shape(3,0), dtype=float64)
>
> To preempt, I'm not really concerned with the answer to: Why would
> anyone want to do this?
>
> I just want to know what is happening. Especially, with
>
x[0
>>> import numpy as np
>>> x = np.ones((3,0))
>>> x
array([], shape(3,0), dtype=float64)
To preempt, I'm not really concerned with the answer to: Why would
anyone want to do this?
I just want to know what is happening. Especially, with
>>> x[0,:] = 5
(which works). It seems that nothing is r
