Travis Oliphant wrote:
> Sebastian Haase wrote:
>
>> Travis Oliphant wrote:
>>
>>
>>
>>> It's not necessarily dead, the problem is complexity of implementation
>>> and more clarity about how all dtypes are supposed to be printed, not
>>> just this particular example. A patch would be
On 9/13/06, Francesc Altet <[EMAIL PROTECTED]> wrote:
> Well, it seems that malloc actually takes more time when asking for more
> space. However, this can't be the reason why Pierre is seeing that:
>
> a = numpy.exp(a) [1]
>
> is slower than
>
> numpy.exp(a,out=a) [2]
>
> as I'd say that this in
Hi gurus,
I'm debugging a C-extension module that uses numpy. My version is 1.0b1.
Can I safely ignore the following compiler warning?
.../lib/python2.4/site-packages/numpy/core/include/numpy/__multiarray_api.h:903:
warning: `_import_array' defined but not used
Any help would be appreciated.
El dj 14 de 09 del 2006 a les 02:11 -0700, en/na Andrew Straw va
escriure:
> >> My main focus is on the fact that you might read ' >> "less" than 4-bytes int, which is very confusing !
> >>
> >>
> > I can agree it's confusing at first, but it's the same syntax the struct
> > module uses wh
Francesc Altet wrote:
>El dj 14 de 09 del 2006 a les 02:11 -0700, en/na Andrew Straw va
>escriure:
>
>
My main focus is on the fact that you might read '>>>"less" than 4-bytes int, which is very confusing !
>>>I can agree it's confusing at first, but it's th
On 9/14/06, Victoria G. Laidler <[EMAIL PROTECTED]> wrote:
Francesc Altet wrote:>El dj 14 de 09 del 2006 a les 02:11 -0700, en/na Andrew Straw va>escriure:>>My main focus is on the fact that you might read '"less" than 4-bytes int, which is very confusing !
>>>I can agree it
Charles R Harris wrote:
>> > Why not simply
>> > write a wrapper function in python that does Numeric-style guesswork,
>> > and put it in the compatibility modules?
>> Can I encourage any more comments?
+1
> The main problem in constructing arrays
> of objects is more information needs to be s
Hi Travis,
Travis Oliphant wrote:
> Ryan Gutenkunst wrote:
>> I notice that numpy_array.item() will give me the first element as a
>> normal scalar. Would it be possible for numpy_array.item(N) to return
>> the Nth element of the array as a normal scalar?
>>
> Now this is an interesting idea.
Ryan Gutenkunst wrote:
>Hi Travis,
>
>Travis Oliphant wrote:
>
>
>>Ryan Gutenkunst wrote:
>>
>>
>>>I notice that numpy_array.item() will give me the first element as a
>>>normal scalar. Would it be possible for numpy_array.item(N) to return
>>>the Nth element of the array as a normal scala
Has anybody had any experience with the 3-D visualization software
VISIT? It has Python bindings and seems to be pretty sophisticated.
I'm wondering why I haven't heard more about it.
http://www.llnl.gov/visit/
-Travis
--
Hi all,
Just wondering if there was an arbitrary axis iterator in numpy, or
if not, if there's demand for one. What I'm looking for is something
which would allow me to do something like (vectorFunc(column) for
column in array.axisIter(1) ) without a bunch of for loops and slicing.
Thoug
Brendan Simons wrote:
>Hi all,
>
>Just wondering if there was an arbitrary axis iterator in numpy, or
>if not, if there's demand for one. What I'm looking for is something
>which would allow me to do something like (vectorFunc(column) for
>column in array.axisIter(1) ) without a bunch of
Iteration over axis 0 is built-in, so you can already do
(vectorFunc(row) for row in array)
And you can use transpose() to make it so the axis you want to iterate
over is axis 0.
(vectorFunc(col) for col in array.transpose(1,0))
Or just use the .T attribute
(vectorFunc(col) for col in a
Travis,
Thanks for the quick response. My application is back up to its old
speed.
Thanks also for spearheading the numpy/scipy projects. It's certainly
made my work much, much more productive.
Cheers,
Ryan
On Sep 14, 2006, at 7:40 PM, Travis Oliphant wrote:
> Ryan Gutenkunst wrote:
>> Thanks
Bill Baxter wrote:
> Iteration over axis 0 is built-in, so you can already do
> (vectorFunc(row) for row in array)
> And you can use transpose() to make it so the axis you want to iterate
> over is axis 0.
> (vectorFunc(col) for col in array.transpose(1,0))
> Or just use the .T attribute
>
Hi,
what I'm asking is if numpy has an equivalent to numarray's info() function:
>>> na.arange(10).info()
class:
shape: (10,)
strides: (4,)
byteoffset: 0
bytestride: 4
itemsize: 4
aligned: 1
contiguous: 1
buffer:
data pointer: 0x085b7ec8 (DEBUG ONLY)
byteorder: 'little'
byteswap: 0
type: Int32
T
On 9/15/06, Tim Hochberg <[EMAIL PROTECTED]> wrote:
> Isn't swapaxis appropriate for this? In other words:
>
You're right. Just didn't think of that. Never used swapaxes before.
def axisiter(arr, i):
return arr.swapaxes(0,i)
--bb
---
Oh that's cool. For some reason I thought that the built in iterator (for i in array) iterated over cells, not the first axis. I also didn't think about swapaxes. Is there any desire to add a convenience function or method as follows?def axisIter(selfOrArr, i): return iter(selfOrArr.swapAxes(0
El dj 14 de 09 del 2006 a les 18:20 -0700, en/na Sebastian Haase va
escriure:
> Especially I'm asking if there is any way to get the memory address of an
> array - for debugging purposes only - of course ;-)
For this, you can print the data buffer:
In [1]:import numpy
In [2]:a=numpy.array([1])
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