Hi,
i am trying to remove nan-values from an array of shape(40, 6).
These nan-values at point data[x] should be replaced by the mean
of data[x-1] and data[x+1] if both values at x-1 and x+1 are not
nan. The function nan_to_mean (see below) is working but i wonder
if i could optimize the code.
I t
On Mon, Aug 12, 2013 at 3:14 PM, Charles R Harris
wrote:
>> > Datetime64 will not be modified in this release.
>>
>> I now there is neither the time nor the will for all that it needs,
>> but please, please, please, can we yank out the broken timezone
>> handling at least?
>>
>> https://github.co
On Mon, Aug 12, 2013 at 4:05 PM, Chris Barker - NOAA Federal <
chris.bar...@noaa.gov> wrote:
> On Mon, Aug 12, 2013 at 2:41 PM, Charles R Harris
> wrote:
>
> > Datetime64 will not be modified in this release.
>
> I now there is neither the time nor the will for all that it needs,
> but please, pl
On Mon, Aug 12, 2013 at 2:41 PM, Charles R Harris
wrote:
> Datetime64 will not be modified in this release.
I now there is neither the time nor the will for all that it needs,
but please, please, please, can we yank out the broken timezone
handling at least?
https://github.com/numpy/numpy/issue
Hi All,
I think we are about ready to start on the 1.8 release. There are a few
things left to do, but there are PR's for most of them. There are a couple
of issues that I think need discussion.
1. Should diagonal return a view in this release or the next?
2. Should multiple field selection
On Mon, Aug 12, 2013 at 10:01 PM, Nicolas Rougier
wrote:
>
>
> Hi,
>
> I have a (n,2) shaped array representing points and I would like to
double each point as in:
>
> A = np.arange(10*2).reshape(10,2)
> B = np.repeat( A, 2, axis=0 )
>
> Is it possible to do the same using 'as_strided' to avoid co
Hi,
I have a (n,2) shaped array representing points and I would like to double each
point as in:
A = np.arange(10*2).reshape(10,2)
B = np.repeat( A, 2, axis=0 )
Is it possible to do the same using 'as_strided' to avoid copy (and still get
the same output shape for B) ?
I found this referenc
Hi Julian
On Mon, Aug 12, 2013 at 4:23 PM, Julian Taylor
wrote:
> The function exposing it is:
> numpy.partition(data, kth=int/array, axis=-1, kind="introselect",
> order=None)
This looks great, thanks very much!
A minor bug was introduced into the Bento build:
https://github.com/numpy/numpy/p
Hi,
a selection algorithm [0] has now landed in the numpy development branch
[1].
The function exposing it is:
numpy.partition(data, kth=int/array, axis=-1, kind="introselect",
order=None)
Please see the docstrings on what it actually does (and report if they
are confusing).
Thanks to the numpy
