On Sun, 2009-06-14 at 15:50 -0500, Robert Kern wrote:
> On Sun, Jun 14, 2009 at 14:31, Bryan Cole wrote:
> > I'm starting work on an application involving cpu-intensive data
> > processing using a quad-core PC. I've not worked with multi-core systems
> > previousl
> In fact, I should have specified previously: I need to
> deploy on MS-Win. On first glance, I can't see that mpi4py is
> installable on Windows.
My mistake. I see it's included in Enthon, which I'm using.
Bryan
>
>
> Bryan
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>
> You may want to look at MPI, e.g. mpi4py is convenient for this kind of
> work. For numerical work across processes it is close to a de facto
> standard.
>
> It requires an MPI implementation set up on your machine though (but for
> single-machine use this isn't hard to set up, typically
I'm starting work on an application involving cpu-intensive data
processing using a quad-core PC. I've not worked with multi-core systems
previously and I'm wondering what is the best way to utilise the
hardware when working with numpy arrays. I think I'm going to use the
multiprocessing package, b
Which (if any) existing ufuncs support the new generalised looping
system? I'm particularly interested in a "vectorised" matrix multiply.
BC
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>
> I think dot will work, though you'll need to work a little bit to get the
> answer:
>
> >>> import numpy as np
> >>> a = np.array([[1,2], [3,4]], np.float)
> >>> aa = np.array([a,a+1,a+2])
> >>> bb = np.array((a*5, a*6, a*7, a*8))
> >>> np.dot(aa, bb).shape
> (3, 2, 4, 2)
> >>> for i, a_ in
I have a number of arrays of shape (N,4,4). I need to perform a
vectorised matrix-multiplication between pairs of them I.e.
matrix-multiplication rules for the last two dimensions, usual
element-wise rule for the 1st dimension (of length N).
(How) is this possible with numpy?
thanks,
BC
_
> > However, also note
> > that with ndarray's rich comparisons, such membership testing will
> > fail with ndarrays, too.
>
> This poses a similarly big problem. I can't understand this behaviour
> either:
OK, I can now. After equality testing each item, the result must be cast
to bool. This is
> >
> > What's the consensus on this? Is the current dtype behaviour broken?
>
> It's suboptimal, certainly. Feel free to fix it.
Thankyou.
> However, also note
> that with ndarray's rich comparisons, such membership testing will
> fail with ndarrays, too.
This poses a similarly big problem.
Dtype objects throw an exception if compared for equality against other
objects. e.g.
>>> import numpy
>>> numpy.dtype('uint32')==1
Traceback (most recent call last):
File "", line 1, in
TypeError: data type not understood
>>>
After some googling, I think python wisdom (given in the Python do
> >> spikes = [(0, 2.3),(1, 5.6),(3, 2.5),(0, 5.2),(3, 10.2),(2, 16.2)]
>
> mysort(spikes)
>
> should return:
>
> [[2.3, 5.2], [5.6], [16.2], [2.5, 10.2]]
>
> Intuitively, the simplest way to do that is to append elements while going
> through all the tuples of the main list (called spikes)
>
>
> From the response, the answer seems to be no, and that I should stick
> with the python loops for clarity. But also, the words of Anne
> Archibald, makes me think that I have made a bad choice by inheriting
> from ndarray, although I am not sure what a convenient alternative
> would be.
On Wed, 2008-04-30 at 21:09 +0200, Gael Varoquaux wrote:
> On Wed, Apr 30, 2008 at 11:57:44AM -0700, Christopher Barker wrote:
> > I think I still like the idea of an iterator (or maybe making rollaxis a
> > method?), but this works pretty well.
>
> Generally, in object oriented programming, you
>
> Well, one thing you could do is dump your data into a PyTables_
> ``CArray`` dataset, which you may afterwards access as if its was a
> NumPy array to get slices which are actually NumPy arrays. PyTables
> datasets have no problem in working with datasets exceeding memory size.
> For instanc
I'm not sure where best to post this, but I get a memory leak when using
code with both numpy and FFT(from Numeric) together:
e.g.
>>> import numpy
>>> import FFT
>>> def test():
... while 1:
... data=numpy.random.random(2048)
... newdata = FFT.real_fft(data)
>>> test()
and m
On Fri, 2007-02-23 at 17:38 +0100, [EMAIL PROTECTED] wrote:
> Hi,
>
> Given a (possibly masked) 2d array x, is there a fast(er) way in Numpy to
> obtain
> the same result as the following few lines?
>
> d = 1 # neighbourhood 'radius'
> Nrow = x.shape[0]
> Ncol =
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