On Fri, 11 Jun 2010, Keith Goodman wrote:
> > For this purpose, I like Fernando's data array:
> > http://github.com/fperez/datarray A very simple subclass of ndarrays
> > that answers my most-wanted feature in terms of richer data
> > structures.
> > >...<
> I looks like datarray labels the axes.
On Fri, Jun 11, 2010 at 22:06, wrote:
> Hi all,
>
> I recently needed to a simple array procedure that I assumed would be
> supported (and almost is) but was surprised to find some limitations.
>
> I've got two arrays, A and B, and some array of indices, I, and I want to
> perform the operation
>
Hi all,
I recently needed to a simple array procedure that I assumed would be
supported (and almost is) but was surprised to find some limitations.
I've got two arrays, A and B, and some array of indices, I, and I want to
perform the operation
A[I] += B
where I and B have the same dimensions.
On Fri, Jun 11, 2010 at 11:51 AM, Gael Varoquaux
wrote:
> On Fri, Jun 11, 2010 at 12:57:37PM -0500, Bruce Southey wrote:
>> 2. Do we really need to build custom data structures (larry, pandas,
>> tabular, etc.) or are structured ndarrays enough? (My conclusion is
>> that we do need to, but othe
On Fri, Jun 11, 2010 at 12:57:37PM -0500, Bruce Southey wrote:
> 2. Do we really need to build custom data structures (larry, pandas,
> tabular, etc.) or are structured ndarrays enough? (My conclusion is
> that we do need to, but others might disagree). If so, how much
> performance are we will
On 06/11/2010 10:26 AM, Wes McKinney wrote:
On Fri, Jun 11, 2010 at 9:46 AM, Bruce Southey wrote:
On 06/09/2010 03:40 PM, Wes McKinney wrote:
Dear all,
We've been having discussions on the pystatsmodels mailing list
recently regarding data structures and other tools for statistics /
Fri, 11 Jun 2010 15:31:45 +0200, Sturla Molden wrote:
[clip]
>> The innermost dimension is handled via the ufunc loop, which is a
>> simple for loop with constant-size step and is given a number of
>> iterations. The array iterator objects are used only for stepping
>> through the outer dimensions.
Den 11.06.2010 17:17, skrev Anne Archibald:
>
> On the other hand, since memory reads are very slow, optimizations
> that do more calculation per load/store could make a very big
> difference, eliminating temporaries as a side effect.
>
Yes, that's the main issue, not the extra memory they use
Sturla Molden wrote:
> Den 11.06.2010 09:14, skrev Sebastien Binet:
>> it of course depends on the granularity at which you wrap and use
>> numpy-core but tight loops calling ctypes ain't gonna be pretty
>> performance-wise.
>>
>
> Tight loops in Python are never pretty.
>
> The purpose of ve
On Fri, Jun 11, 2010 at 9:46 AM, Bruce Southey wrote:
> On 06/09/2010 03:40 PM, Wes McKinney wrote:
>> Dear all,
>>
>> We've been having discussions on the pystatsmodels mailing list
>> recently regarding data structures and other tools for statistics /
>> other related data analysis applications.
On 11 June 2010 11:12, Benjamin Root wrote:
>
>
> On Fri, Jun 11, 2010 at 8:31 AM, Sturla Molden wrote:
>>
>>
>> It would also make sence to evaluate expressions like "y = b*x + a"
>> without a temporary array for b*x. I know roughly how to do it, but
>> don't have time to look at it before next
On Fri, Jun 11, 2010 at 8:31 AM, Sturla Molden wrote:
>
>
> It would also make sence to evaluate expressions like "y = b*x + a"
> without a temporary array for b*x. I know roughly how to do it, but
> don't have time to look at it before next year. (Yes I know about
> numexpr, I am talking about p
Den 11.06.2010 09:14, skrev Sebastien Binet:
> it of course depends on the granularity at which you wrap and use
> numpy-core but tight loops calling ctypes ain't gonna be pretty
> performance-wise.
>
Tight loops in Python are never pretty.
The purpose of vectorization with NumPy is to avoid
On 06/09/2010 03:40 PM, Wes McKinney wrote:
> Dear all,
>
> We've been having discussions on the pystatsmodels mailing list
> recently regarding data structures and other tools for statistics /
> other related data analysis applications. I believe we're trying to
> answer a number of different, bu
Den 11.06.2010 10:17, skrev Pauli Virtanen:
>> 1. Collect an array of pointers to each subarray (e.g. using
>> std::vector or dtype**)
>> 2. Dispatch on the pointer array...
>>
> This is actually what the current ufunc code does.
>
> The innermost dimension is handled via the ufunc loop, whi
On Friday 11 June 2010 10:38:28 Pauli Virtanen wrote:
> Fri, 11 Jun 2010 10:29:28 +0200, Hans Meine wrote:
> > Ideally, algorithms would get wrapped in between two additional
> > pre-/postprocessing steps:
> >
> > 1) Preprocessing: After broadcasting, transpose the input arrays such
> > that they
Fri, 11 Jun 2010 10:29:28 +0200, Hans Meine wrote:
[clip]
> Ideally, algorithms would get wrapped in between two additional
> pre-/postprocessing steps:
>
> 1) Preprocessing: After broadcasting, transpose the input arrays such
> that they become C order. More specifically, sort the strides of one
On Thursday 10 June 2010 22:28:28 Pauli Virtanen wrote:
> Some places where Openmp could probably help are in the inner ufunc
> loops. However, improving the memory efficiency of the data access
> pattern is another low-hanging fruit for multidimensional arrays.
I was about to mention this when th
Thu, 10 Jun 2010 23:56:56 +0200, Sturla Molden wrote:
[clip]
> Also about array iterators in NumPy's C base (i.e. for doing something
> along an axis): we don't need those. There is a different way of coding
> which leads to faster code.
>
> 1. Collect an array of pointers to each subarray (e.g. u
A Friday 11 June 2010 02:27:18 Sturla Molden escrigué:
> >> Another thing I did when reimplementing lfilter was "copy-in copy-out"
> >> for strided arrays.
> >
> > What is copy-in copy out ? I am not familiar with this term ?
>
> Strided memory access is slow. So it often helps to make a temporary
On Fri, 11 Jun 2010 00:25:17 +0200, Sturla Molden wrote:
> Den 10.06.2010 22:07, skrev Travis Oliphant:
> >
> >> 2. The core should be a plain DLL, loadable with ctypes. (I know David
> >> Cournapeau and Robert Kern is going to hate this.) But if Python can have
> >> a custom loader for .pyd fil
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