Niels Provos wrote:
> Good morning,
>
> not sure if I got the right list, but I hope that somebody here will
> be able to shed some light on a Python-related memory problem. The
> following code eats over >2GB of memory and fails with MemoyError
> after just a few iterations.
Here is a minimal e
On 2/5/07, Tim Leslie <[EMAIL PROTECTED]> wrote:
> Hi All,
>
> As of svn revision 3530 N.nan_to_num no longer works for arrays of
> complex64. The actual error is raised in the signbit function, but I'm
> not sure why this is failing. If someone has a quick fix for this
> that'd be great, if not I'
On Sunday 04 February 2007 20:22:44 Jeremy Conlin wrote:
> I have subclassed the numpy.ndarray object, but need some help setting
> some attributes. I have read http://scipy.org/Subclasses but it
> doesn't provide the answer I am looking for.
Actually, yes:
In the example given in http://scipy.or
On 2/4/07, Robert Kern <[EMAIL PROTECTED]> wrote:
> Keith Goodman wrote:
> > There's a numpy.abs but no numpy.matlib.abs.
> >
> >>> import numpy as N
> >>> import numpy.matlib as M
> >>>
> >>> N.abs?
> > Type: ufunc
> > Base Class:
> > String Form:
> > Namespace: Interactive
I have subclassed the numpy.ndarray object, but need some help setting
some attributes. I have read http://scipy.org/Subclasses but it
doesn't provide the answer I am looking for.
I create an instance of the class in my __new__ method as:
import numpy
class MyClass(numpy.ndarray):
__new__(self,
Keith Goodman wrote:
> There's a numpy.abs but no numpy.matlib.abs.
>
>>> import numpy as N
>>> import numpy.matlib as M
>>>
>>> N.abs?
> Type: ufunc
> Base Class:
> String Form:
> Namespace: Interactive
> Docstring:
> y = absolute(x) takes |x| elementwise.
>
>>> M.abs
There's a numpy.abs but no numpy.matlib.abs.
>> import numpy as N
>> import numpy.matlib as M
>>
>> N.abs?
Type: ufunc
Base Class:
String Form:
Namespace: Interactive
Docstring:
y = absolute(x) takes |x| elementwise.
>> M.abs?
Object `M.abs` not found.
Sebastian Haase wrote:
> Is there *any* support for old Numeric on this list !?
Not unless if you are offering some.
> Maybe it should be officially stated that the one way to go is
> numpy
> and that problems with Numeric ( or numarray ) can only be noticed but
> will likely not get fixed
T
On 2/4/07, Robert Kern <[EMAIL PROTECTED]> wrote:
> Niels Provos wrote:
> > The missing imports are
> >
> > import Numeric # for zeros and ones
> > from scipy.fftpack import fft2,ifft2
> >
> > Curiously, replacing Numeric.zeros with scipy.zeros makes the problem
> > go away. Why?
>
> Possibly a bu
Hi,
I'm using numpy svn and running numpy.test().
The error log is the following :
==
ERROR: check_complex_bad (numpy.lib.tests.test_type_check.test_nan_to_num)
---
Niels Provos wrote:
> The missing imports are
>
> import Numeric # for zeros and ones
> from scipy.fftpack import fft2,ifft2
>
> Curiously, replacing Numeric.zeros with scipy.zeros makes the problem
> go away. Why?
Possibly a bug in Numeric.
--
Robert Kern
"I have come to believe that the wh
The missing imports are
import Numeric # for zeros and ones
from scipy.fftpack import fft2,ifft2
Curiously, replacing Numeric.zeros with scipy.zeros makes the problem
go away. Why?
Thank you,
Niels.
On 2/4/07, Robert Kern <[EMAIL PROTECTED]> wrote:
> Niels Provos wrote:
> > Good morning,
> >
Niels Provos wrote:
> Good morning,
>
> not sure if I got the right list, but I hope that somebody here will
> be able to shed some light on a Python-related memory problem. The
> following code eats over >2GB of memory and fails with MemoyError
> after just a few iterations.
>
> def ZeroPadData
On 2/4/07, Sebastian Haase <[EMAIL PROTECTED]> wrote:
> On 2/3/07, Robert Kern <[EMAIL PROTECTED]> wrote:
> > Stephen Simmons wrote:
> >
> > > The question though is whether all of the inner loop's overhead is
> > > necessary.
> > > My counterexample using numpy.dot() suggests there's considerable
On 2/3/07, Robert Kern <[EMAIL PROTECTED]> wrote:
> Stephen Simmons wrote:
>
> > The question though is whether all of the inner loop's overhead is
> > necessary.
> > My counterexample using numpy.dot() suggests there's considerable scope
> > for improvement, at least for certain common cases.
>
>
Good morning,
not sure if I got the right list, but I hope that somebody here will
be able to shed some light on a Python-related memory problem. The
following code eats over >2GB of memory and fails with MemoyError
after just a few iterations.
def ZeroPadData(A, shape):
a = Numeric.zeros(sh
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