Nevermind, I just found http://bugs.python.org/issue1675423 .
On Jul 9, 2009, at 1:41 AM, Robert Bradshaw wrote:
> I know using __complex__ has been discussed before, but it would be
> really nice if it were at least used to convert object to the
> complex dtypes.
>
> - Robert
>
>
> In [1]: c
actually what would be better is if i can pass two 1d arrays X and Y
both size Nx1
and get back a 2d array of size NxM where the [n,:] row is the linear
interpolation of X[n] to Y[n]
On Fri, Jul 10, 2009 at 1:16 AM, Chris Colbert wrote:
> If i have two arrays representing start points and end po
If i have two arrays representing start points and end points, is
there a function that will return a 2d array where each row is the
range(start, end, n) where n is a fixed number of steps and is the
same for all rows?
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Nu
hey,
great man! thanks!
I had thought that it may have been possible with a single dot, but
how to do it escaped me.
Thanks again!
Chris
On Thu, Jul 9, 2009 at 11:45 PM, Kurt Smith wrote:
> On Thu, Jul 9, 2009 at 9:36 PM, Chris Colbert wrote:
>> no, because dot(x,y) != dot(y,x)
>>
>
> Try this
On Thu, Jul 9, 2009 at 9:36 PM, Chris Colbert wrote:
> no, because dot(x,y) != dot(y,x)
>
Try this, then:
In [24]: x = np.random.rand(10,4)
In [25]: y = np.random.rand(4,4)
In [26]: result = np.dot(y,x.T).T
In [39]: for i, res in enumerate(result):
: assert np.allclose(res, np.dot(y
no, because dot(x,y) != dot(y,x)
>>> x = np.random.rand(3,4)
>>> y = np.random.rand(4,4)
>>> np.dot(x, y)
array([[ 1.67624043, 1.66719374, 1.72465017, 1.20372021],
[ 0.70046162, 0.60187869, 0.73094349, 0.4604766 ],
[ 0.78707401, 1.01959666, 0.61617829, 0.43147398]])
>>> np.d
On Thu, Jul 9, 2009 at 7:08 PM, Chris Colbert wrote:
> say i have an Nx4 array of points and I want to dot every [n, :] 1x4
> slice with a 4x4 matrix.
>
> Currently I am using apply_along_axis in the following manner:
>
> def func(slice, mat):
> return np.dot(mat, slice)
>
> np.apply_along_axis
say i have an Nx4 array of points and I want to dot every [n, :] 1x4
slice with a 4x4 matrix.
Currently I am using apply_along_axis in the following manner:
def func(slice, mat):
return np.dot(mat, slice)
np.apply_along_axis(func, arr, 1, mat)
Is there a more efficient way of doing this th
I tried to implement what you suggest.
The patch is in the ticket page.
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On Thu, Jul 9, 2009 at 15:20, Fons Adriaensen wrote:
> On Thu, Jul 09, 2009 at 12:00:23PM -0500, Robert Kern wrote:
>
>> On Thu, Jul 9, 2009 at 11:44, Fons Adriaensen wrote:
>>
>> > There is a simple rule which says that if you use an object
>> > pointer as a function argument you must INCREF it. T
>> Hello all,
>>
>> (resending for the Nth time, as the previous attempts
>> didn't make it to the list)
>>
>> I'm new to this list (and numpy is mostly new to me :-).
>>
>> Using python 2.6 and numpy 1.3.
>>
>> My plan is to write some C extensions that will perform
>> rather specialised processi
On Thu, Jul 09, 2009 at 12:00:23PM -0500, Robert Kern wrote:
> On Thu, Jul 9, 2009 at 11:44, Fons Adriaensen wrote:
>
> > There is a simple rule which says that if you use an object
> > pointer as a function argument you must INCREF it. This is
> > just the logical consequence of using refcounted
On 2009-07-09, Citi, Luca wrote:
> Let me see if I understand correctly...
> what you suggest is something like:
> 1) adding an argument flag to construct_arrays
> that enables/disables the feature
> 2) adding the same argument flag to construct_loop which
> is passed untouched to construct_ar
Make sure you send the message as plain text, rather than with rich
formatting, and it should show up right away.
Mark
On Thu, Jul 9, 2009 at 11:18 AM, Ian Stokes-Rees
wrote:
>
> [take 6 on sending this -- I'm subscribed to numpy-discuss, but this
> post refuses to show up]
>
>
_
Let me see if I understand correctly...
what you suggest is something like:
1) adding an argument flag to construct_arrays
that enables/disables the feature
2) adding the same argument flag to construct_loop which
is passed untouched to construct_arrays
3) set the flag to "disable" in the const
[take 6 on sending this -- I'm subscribed to numpy-discuss, but this
post refuses to show up]
I have an large array consisting of 5-tuples. I'd like to select the
first and second columns in order to produce a scatter plot. Each tuple
consists of mixed types (floats and strings). The Matlab e
This showed up on numpy-discussion. I posted this morning and it hasn't
appeared yet.
Ian
Nadav Horesh wrote:
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>
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Ian St
On Thu, Jul 9, 2009 at 11:44, Fons Adriaensen wrote:
> On Thu, Jul 09, 2009 at 04:30:11PM +0100, Citi, Luca wrote:
>
>> Also one could have an internal flag which says whether or not is safe
>> to overwrite inputs with ref_count=1.
>> Then import_array() sets this flag to "unsafe" (i.e. current beh
there's a wrapper called PyCuda. but you actually write cuda code as a
docstring and its compiled and exectuted at run time.
I think it can be done more pythonic.
On Thu, Jul 9, 2009 at 12:31 PM, Gökhan SEVER wrote:
> Still speaking of particles, has anyone seen Nvidia's Cuda powered particle
>
On Thu, Jul 09, 2009 at 04:30:11PM +0100, Citi, Luca wrote:
> Also one could have an internal flag which says whether or not is safe
> to overwrite inputs with ref_count=1.
> Then import_array() sets this flag to "unsafe" (i.e. current behaviour).
> If the user of the numpy C-api is aware of how t
2009/7/9 Nadav Horesh :
> The ndimage package can be accessed as numpy.numarray.nd_image. scipy is not
> needed
numpy.numarray.nd_image just imports from scipy.ndimage or a
standalone ndimage if you have built it so. numpy does not contain the
ndimage code.
--
Robert Kern
"I have come to belie
The ndimage package can be accessed as numpy.numarray.nd_image. scipy is not
needed
Nadav
-הודעה מקורית-
מאת: numpy-discussion-boun...@scipy.org בשם Thomas Hrabe
נשלח: ה 09-יולי-09 17:57
אל: Discussion of Numerical Python
נושא: Re: [Numpy-discussion] interpolation in numpy
Yep,
that
Still speaking of particles, has anyone seen Nvidia's Cuda powered particle
systme demo?
http://www.youtube.com/watch?v=RqduA7myZok
I have a CUDA supported graphics card on my laptop, and tested on the actual
hardware. Seems very cool. However has way too many lines of code for that
piece of demo
On Thu, Jul 09, 2009 at 10:41:38AM -0500, Robert Kern wrote:
> We could change ufunc_generic_call() (i.e. the C implementation of
> ufunc.__call__) to use a new function like PyUFunc_GenericFunction
> except with the refcount-1 semantics. This allows old C-level to
> remain unchanged but let Python
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On Thu, Jul 9, 2009 at 04:35, Pauli Virtanen wrote:
> Thu, 09 Jul 2009 10:03:47 +0100, Citi, Luca kirjoitti:
> [clip]
>> Excuse me if I insist, PyArray_Conjugate is not the problem. If when
>> using the numpy API, it is accepted something like:
>>
>> obj1 = PyArray_CreateSomehowAnArray();
>
On Thu, Jul 9, 2009 at 10:30, Citi, Luca wrote:
> Hello Gaël,
>
> I think it might be an option.
>
> Also one could have an internal flag which says whether or not is safe
> to overwrite inputs with ref_count=1.
> Then import_array() sets this flag to "unsafe" (i.e. current behaviour).
> If the use
Hello Gaël,
I think it might be an option.
Also one could have an internal flag which says whether or not is safe
to overwrite inputs with ref_count=1.
Then import_array() sets this flag to "unsafe" (i.e. current behaviour).
If the user of the numpy C-api is aware of how the new feature works,
he
Yep,
thats the one.
Unfortunately, installing scipy makes my project dependant on another
package. However, I installed it and it works...
Thank you
2009/7/9 Joe Kington :
> scipy.ndimage.zoom is exactly what you're looking for, as Zach Pincus
> already said.
>
> As far as I know, numpy doesn't ha
scipy.ndimage.zoom is exactly what you're looking for, as Zach Pincus
already said.
As far as I know, numpy doesn't have any 3D interpolation routines, so
you'll have to install scipy. Interp2d will only interpolate slices of your
data, not the whole volume.
-Joe
On Thu, Jul 9, 2009 at 8:42 AM,
On Thu, Jul 09, 2009 at 09:35:06AM +, Pauli Virtanen wrote:
> Thu, 09 Jul 2009 10:03:47 +0100, Citi, Luca kirjoitti:
> [clip]
> > Excuse me if I insist, PyArray_Conjugate is not the problem. If when
> > using the numpy API, it is accepted something like:
> > obj1 = PyArray_CreateSomehowAnA
You might want also to look into scipy.ndimage.zoom.
Zach
On Jul 9, 2009, at 9:42 AM, Thomas Hrabe wrote:
> Hi all,
>
> I am not a newbie to python and numpy, but however, I kinda do not
> find a proper solution for my interpolation problem without coding it
> explicitly myself.
>
> All I want t
Hi,
you can have a look at the method interp2d of scipy.interpolate.
I think it is what you are looking for.
Best,
Luca
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Hi all,
I am not a newbie to python and numpy, but however, I kinda do not
find a proper solution for my interpolation problem without coding it
explicitly myself.
All I want to do is to increase the resolution of an tree dimensional array.
I have a volume 'a'
a = numpy.random.rand(3,3,3)
now,
The problem is the array is very large. We are talking about 200+ million rows.
On Thu, Jul 9, 2009 at 4:41 AM, David Warde-Farley wrote:
> On 9-Jul-09, at 1:12 AM, Mag Gam wrote:
>
>> Here is what I have, which does it 1x1:
>>
>> z={} #dictionary
>> r=csv.reader(file)
>> for i,row in enumerate(
Thu, 09 Jul 2009 10:03:47 +0100, Citi, Luca kirjoitti:
[clip]
> Excuse me if I insist, PyArray_Conjugate is not the problem. If when
> using the numpy API, it is accepted something like:
>
> obj1 = PyArray_CreateSomehowAnArray();
> obj2 = PyArray_DoSomethingWithArray(obj1,...);
> o
Thu, 09 Jul 2009 09:54:26 +0200, Matthieu Brucher kirjoitti:
> 2009/7/9 Pauli Virtanen :
[clip]
>> I'm still kind of hoping that it's possible to make some minimal
>> assumptions about CPU caches in general, and have a rule that decides a
>> code path that is good enough, if not optimal.
>
> Unfor
Hello Pauli,
excuse me if I insist, PyArray_Conjugate is not the problem.
If when using the numpy API, it is accepted something like:
ob1 = PyArray_CreateSomehowAnArray();
obj2 = PyArray_DoSomethingWithArray(obj1,...);
obj3 = PyArray_DoSomethingElseWithArray(obj1,...);
Py_DECREF(obj1);
then
2009/7/9 David Cournapeau :
> Matthieu Brucher wrote:
>>
>> Unfortunately, this is not possible. We've been playing with blocking
>> loops for a long time in finite difference schemes, and it is always
>> compiler dependent
>
> You mean CPU dependent, right ? I can't see how a reasonable optimizing
On 8-Jul-09, at 6:16 PM, Pauli Virtanen wrote:
> Just to tickle some interest, a "pathological" case before
> optimization:
>
>In [1]: import numpy as np
>In [2]: x = np.zeros((8, 256))
>In [3]: %timeit x.sum(axis=0)
>10 loops, best of 3: 850 ms per loop
>
> After optimizatio
Matthieu Brucher wrote:
>
> Unfortunately, this is not possible. We've been playing with blocking
> loops for a long time in finite difference schemes, and it is always
> compiler dependent
You mean CPU dependent, right ? I can't see how a reasonable optimizing
compiler could make a big difference
Thu, 09 Jul 2009 10:00:25 +0200, Matthieu Brucher kirjoitti:
> 2009/7/9 Citi, Luca :
>> Hello
>>
>> The problem is not PyArray_Conjugate itself. The problem is that
>> whenever you call a function from the C side and one of the inputs has
>> ref_count 1, it can be overwritten. This is not a proble
On 9-Jul-09, at 1:12 AM, Mag Gam wrote:
> Here is what I have, which does it 1x1:
>
> z={} #dictionary
> r=csv.reader(file)
> for i,row in enumerate(r):
> p="/MIT/"+row[1]
>
> if p not in z:
>z[p]=0:
> else:
>z[p]+=1
>
> arr[p]['chem'][z[p]]=tuple(row) #this loads the array 1 x 1
>
>
> Hello all,
>
> (resending for the Nth time, as the previous attempts
> didn't make it to the list)
>
> I'm new to this list (and numpy is mostly new to me :-).
>
> Using python 2.6 and numpy 1.3.
>
> My plan is to write some C extensions that will perform
> rather specialised processing on multi
2009/7/9 Citi, Luca :
> Hello
>
> The problem is not PyArray_Conjugate itself.
> The problem is that whenever you call a function from the C side
> and one of the inputs has ref_count 1, it can be overwritten.
> This is not a problem from the python side because if the
> ufunc sees a ref_count=1 it
2009/7/9 Pauli Virtanen :
> On 2009-07-08, Stéfan van der Walt wrote:
>> I know very little about cache optimality, so excuse the triviality of
>> this question: Is it possible to design this loop optimally (taking
>> into account certain build-time measurable parameters), or is it the
>> kind of
On Wed, Jul 8, 2009 at 10:39 PM, wrote:
>>
>>>On Fri, Jul 3, 2009 at 10:21 PM, Alan Jackson wrote:
>>>
>>>I don't see any problem here. If you can replicate your results, we
>>>would need more information about the versions.
>>>
>>>Josef
>>>
>>>'''
>> np.version.version
>>>'1.3.0'
>>
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