On 09/11/06, Paul Dubois <[EMAIL PROTECTED]> wrote:
> Since the function of old retired persons is to tell youngsters stories
> around the campfile:
I'll pull up a log. But since I'm uppity, and not especially young, I
hope you won't mind if I heckle.
> A supercomputer hardware designer told me
Disappointed in NaN land?Since the function of old retired persons is to tell youngsters stories around the campfile:A supercomputer hardware designer told me that when forced to add IEEE arithmetic to his designs that it decreased performance substantially, maybe 25-30%; it wasn't that doing the o
Christopher Barker wrote:
> Tim Hochberg wrote:
>
>> I've been told that operations on NANs are slow because they aren't
>> always implemented in the FPU hardware. Instead they are trapped and
>> implemented software or firmware or something or other.
>>
>
> which still doesn't make sens
Tim Hochberg wrote:
> I've been told that operations on NANs are slow because they aren't
> always implemented in the FPU hardware. Instead they are trapped and
> implemented software or firmware or something or other.
which still doesn't make sense -- doesn't ANY operation with a NaN
return N
It looks like on my Pentium M multiplication with NaNs is slow, but
using a masked array ranges from slightly faster (with only one value
masked) to twice as slow (with all values masked):
In [15]:Timer("a.prod()", "import numpy as np; aa = np.ones(4096); a =
np.ma.masked_greater(aa,0)").timei
A. M. Archibald wrote:
> On 08/11/06, Tim Hochberg <[EMAIL PROTECTED]> wrote:
>
>
>> It has always been my experience (on various flavors or Pentium) that
>> operating on NANs is extremely slow. Does anyone know on what hardware
>> NANs are *not* slow? Of course it's always possible I just never
On 08/11/06, Tim Hochberg <[EMAIL PROTECTED]> wrote:
> It has always been my experience (on various flavors or Pentium) that
> operating on NANs is extremely slow. Does anyone know on what hardware
> NANs are *not* slow? Of course it's always possible I just never notice
> NANs on hardware where t
A. M. Archibald wrote:
> On 08/11/06, Pierre GM <[EMAIL PROTECTED]> wrote:
>
>
>> I like your idea, but not its implementation. If MA.masked_singleton is
>> defined as an object, as you suggest, then the dtype of the ndarray it is
>> passed to becomes 'object', as you pointed out, and that is no
2 cents from the author of the first folio:The intent was to allow creation of masked arrays with copy=no, so that the original data could be retrieved from it if desired. But I was quite, quite rigorous about NEVER assuming the data in a masked slot made any sense whatsoever.
The intention was tha
> A good candidate for "should be masked" marked is NaN. It is supposed
> to mean, more or less, "no sensible value".
Which might turn out out to be the best indeed. Michael's application would
then look like
>>> import numpy as N
>>> import maskedarray as MA
>>> maskit = N.nan
>>> test = N.arra
On 08/11/06, Pierre GM <[EMAIL PROTECTED]> wrote:
> I like your idea, but not its implementation. If MA.masked_singleton is
> defined as an object, as you suggest, then the dtype of the ndarray it is
> passed to becomes 'object', as you pointed out, and that is not something one
> would naturally
Michael,
First of all, thanks for your interest in the exercise of style the new
implementation of MaskedArray is basically nothing but.
On Tuesday 07 November 2006 20:11, Michael Sorich wrote:
> 1. It would be nice if the masked_singleton could be passed into a
> ndarray, as this would allow it
On 10/25/06, Pierre GM <[EMAIL PROTECTED]> wrote:
> On Tuesday 24 October 2006 02:50, Michael Sorich wrote:
> > I am currently running numpy rc2 (I haven't tried your
> > reimplementation yet as I am still using python 2.3). I am wondering
> > whether the new maskedarray is able to handle construct
On Tuesday 24 October 2006 02:50, Michael Sorich wrote:
> I am currently running numpy rc2 (I haven't tried your
> reimplementation yet as I am still using python 2.3). I am wondering
> whether the new maskedarray is able to handle construction of arrays
> from masked scalar values (not sure if thi
I am currently running numpy rc2 (I haven't tried your
reimplementation yet as I am still using python 2.3). I am wondering
whether the new maskedarray is able to handle construction of arrays
from masked scalar values (not sure if this is the correct term). I
ran across a situation recently when I
Folks,
I updated the alternative implementation of MaskedArray on the wiki, mainly to
correct a couple of bugs.
(http://projects.scipy.org/scipy/numpy/wiki/MaskedArray)
In addition, I attached another file, maskedrecordarray, which introduce a new
class, MaskedRecord, as a subclass of recarray
Folks,I updated the alternative implementation of MaskedArray on the wiki, mainly to correct a couple of bugs. (http://projects.scipy.org/scipy/numpy/wiki/MaskedArray
)In addition, I attached another file, maskedrecordarray, which introduce a new class, MaskedRecord, as a subclass of recarray and M
On 10/17/06, Pierre GM <[EMAIL PROTECTED]> wrote:
> On Monday 16 October 2006 22:08, Michael Sorich wrote:
> > Does this new MA class allow masking of rearray like arrays?
>
> Excellent question! Which is to say, I have no idea... I don't use
> recordarray, so I didn't think about testing them.
>
>
On Monday 16 October 2006 22:08, Michael Sorich wrote:
> Does this new MA class allow masking of rearray like arrays?
Excellent question! Which is to say, I have no idea... I don't use
recordarray, so I didn't think about testing them.
So, a first test indicates that it doesn't work either. The
Does this new MA class allow masking of rearray like arrays? The numpy
(1.0b5) version does not seem to. e.g.
from numpy import *
desc = [('name','S30'),('age',int8),('weight',float32)]
a = array([('Bill',31,260.0),('Fred', 15, 145.0)], dtype=desc)
print a[0]
print a['name']
a2 = ma.array([('Bil
Folks,
I just posted on the scipy/developers zone wiki
(http://projects.scipy.org/scipy/numpy/wiki/MaskedArray) a reimplementation
of the masked_array mopdule, motivated by some problems I ran into while
subclassing MaskedArray.
The main differences with the initial numpy.core.ma package are t
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