Oh,
is this actually documented - I knew that np.array would (by default)
only create copies as need ... but I never knew it would - if all fits
- even just return the original Python-object...
Thanks,
Sebastian Haase
On Sat, Sep 22, 2012 at 8:12 PM, Travis Oliphant wrote:
> Check to see if this
On Sat, Sep 22, 2012 at 12:19 PM, Charles R Harris <
charlesr.har...@gmail.com> wrote:
>
>
> On Sat, Sep 22, 2012 at 12:06 PM, Olivier Grisel > wrote:
>
>> 2012/9/22 Charles R Harris :
>> >
>> >
>> > On Sat, Sep 22, 2012 at 11:52 AM, Charles R Harris
>> > wrote:
>> >>
>> >>
>> >>
>> >> On Sat, S
Ooops obviously thanks a lot, stupid me. Thanks was also enough to
figure the rest out myself...
On Sat, 2012-09-22 at 13:12 -0500, Travis Oliphant wrote:
> Check to see if this expression is true
>
> no is o
>
> In the first case no and o are the same object
>
>
> Travis
>
> --
> Travis Oli
On Sat, Sep 22, 2012 at 12:06 PM, Olivier Grisel
wrote:
> 2012/9/22 Charles R Harris :
> >
> >
> > On Sat, Sep 22, 2012 at 11:52 AM, Charles R Harris
> > wrote:
> >>
> >>
> >>
> >> On Sat, Sep 22, 2012 at 11:31 AM, Gael Varoquaux
> >> wrote:
> >>>
> >>> On Sat, Sep 22, 2012 at 11:16:27AM -0600,
Check to see if this expression is true
no is o
In the first case no and o are the same object
Travis
--
Travis Oliphant
(on a mobile)
512-826-7480
On Sep 22, 2012, at 1:01 PM, Sebastian Berg wrote:
> Hi,
>
> I have a bit of trouble figuring this out. I would have expected
> np.asarray(a
2012/9/22 Charles R Harris :
>
>
> On Sat, Sep 22, 2012 at 11:52 AM, Charles R Harris
> wrote:
>>
>>
>>
>> On Sat, Sep 22, 2012 at 11:31 AM, Gael Varoquaux
>> wrote:
>>>
>>> On Sat, Sep 22, 2012 at 11:16:27AM -0600, Charles R Harris wrote:
>>> >I think this is a bug, taking a view should prob
Hi,
I have a bit of trouble figuring this out. I would have expected
np.asarray(array) to go through ctors, PyArray_NewFromArray, but it
seems to me it does not, so which execution path is exactly taken here?
The reason I am asking is that I want to figure out this behavior/bug,
and I really am no
On Sat, Sep 22, 2012 at 11:52 AM, Charles R Harris <
charlesr.har...@gmail.com> wrote:
>
>
> On Sat, Sep 22, 2012 at 11:31 AM, Gael Varoquaux <
> gael.varoqu...@normalesup.org> wrote:
>
>> On Sat, Sep 22, 2012 at 11:16:27AM -0600, Charles R Harris wrote:
>> >I think this is a bug, taking a vie
On Sat, Sep 22, 2012 at 11:31 AM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> On Sat, Sep 22, 2012 at 11:16:27AM -0600, Charles R Harris wrote:
> >I think this is a bug, taking a view should probably update the
> offset.
>
> OK, we can include a fix for that alongside with the pat
Hey,
Numpy currently assumes that if "ndim > 1" then it is impossible for any
array to be both C- and F-contiguous, however an axes of dimension 1
does have no effect on the memory layout. I think I have made most
important changes (actually really very few), though I bet some parts of
numpy still
On Sat, Sep 22, 2012 at 11:16:27AM -0600, Charles R Harris wrote:
>I think this is a bug, taking a view should probably update the offset.
OK, we can include a fix for that alongside with the patch to keep track
of the filename.
Cheers,
Gaël
___
Nu
On Sat, Sep 22, 2012 at 06:30:27PM +0200, Olivier Grisel wrote:
> The only clean solution would be to make `numpy.memmap` use a wrapper
> buffer object that would keep track of the filename and offset
> attributes instead of using a `mmap.mmap` instance directly.
Indeed, Olivier and I have been st
On Sat, Sep 22, 2012 at 7:54 AM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> Hi list,
>
> I am struggling with offsets on the view of a memmaped array. Consider
> the following:
>
> import numpy as np
>
> a = np.memmap('tmp.mmap', dtype=np.float64, shape=50, mode='w+')
> a[:] = np.ara
A posix dup (http://www.unix.com/man-page/POSIX/3posix/dup/) would not
solve it as the fd is hidden inside the python `mmap.mmap` instance
that is a builtin that just exposes the python buffer interface and
hides the implementation details.
The only clean solution would be to make `numpy.memmap` u
On Sat, Sep 22, 2012 at 10:15:52AM -0600, Charles R Harris wrote:
>Would some sort of 'dup' method be useful?
What do you mean by dup?
G
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On Sat, Sep 22, 2012 at 9:46 AM, Olivier Grisel wrote:
> There is also a third use case that is problematic on numpy master:
>
> orig = np.memmap('tmp.mmap', dtype=np.float64, shape=100, mode='w+')
> orig[:] = np.arange(orig.shape[0]) * -1.0 # negative markers to
> detect under / overflows
>
> a
There is also a third use case that is problematic on numpy master:
orig = np.memmap('tmp.mmap', dtype=np.float64, shape=100, mode='w+')
orig[:] = np.arange(orig.shape[0]) * -1.0 # negative markers to
detect under / overflows
a = np.memmap('tmp.mmap', dtype=np.float64, shape=50, mode='r+', offse
2012/9/22 Gael Varoquaux :
> Hi list,
>
> I am struggling with offsets on the view of a memmaped array. Consider
> the following:
>
> import numpy as np
>
> a = np.memmap('tmp.mmap', dtype=np.float64, shape=50, mode='w+')
> a[:] = np.arange(50)
> b = a[10:]
>
> Here, I have a.offset == 0 and b.offs
Hi list,
I am struggling with offsets on the view of a memmaped array. Consider
the following:
import numpy as np
a = np.memmap('tmp.mmap', dtype=np.float64, shape=50, mode='w+')
a[:] = np.arange(50)
b = a[10:]
Here, I have a.offset == 0 and b.offset == 0. In practice, the data in b
is offset c
On Fri, Sep 21, 2012 at 11:39 PM, Nathaniel Smith wrote:
> On Fri, Sep 21, 2012 at 9:42 PM, Ralf Gommers
> wrote:
> > Eh, just installing numpy with "python setup.py install" uses plain
> > distutils, not setuptools. So there indeed isn't an entry in
> > easy-install.pth. Which some consider a
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