On May 2, 2012, at 10:03 PM, Stéfan van der Walt wrote:
> On Wed, May 2, 2012 at 6:25 PM, Travis Oliphant wrote:
>> The only new principle (which is not strictly new --- but new to NumPy's
>> world-view) is using one (or more) fields of a structured array as
>> "synthetic dimensions" which rep
On Wed, May 2, 2012 at 3:20 PM, Nathaniel Smith wrote:
> On Wed, May 2, 2012 at 9:53 PM, Francesc Alted
> wrote:
> > On 5/2/12 11:16 AM, Wolfgang Kerzendorf wrote:
> >> Hi all,
> >>
> >> I'm currently writing a code that needs three dimensional data (for the
> physicists it's dimensions are atom
On Wed, May 2, 2012 at 6:25 PM, Travis Oliphant wrote:
> The only new principle (which is not strictly new --- but new to NumPy's
> world-view) is using one (or more) fields of a structured array as "synthetic
> dimensions" which replace 1 or more of the raw table dimensions.
Ah, thanks--that's
On Wed, May 2, 2012 at 6:46 PM, Kevin Jacobs
wrote:
> On Wed, May 2, 2012 at 7:25 PM, Aronne Merrelli
> wrote:
>>
>> In general this is a good suggestion - I was going to mention it
>> earlier - but I think for this particular problem it is not better
>> than the "brute force" and argmin() NumPy
On Wednesday, May 2, 2012, Stéfan van der Walt wrote:
> On Wed, May 2, 2012 at 4:46 PM, Kevin Jacobs
>
> >
> > wrote:
> > A FLANN implementation should be even faster--perhaps by as much as
> another
> > factor of two.
>
> I guess it depends on whether you care about the "Approximate" in
> "Fast
On Wed, May 2, 2012 at 4:46 PM, Kevin Jacobs
wrote:
> A FLANN implementation should be even faster--perhaps by as much as another
> factor of two.
I guess it depends on whether you care about the "Approximate" in
"Fast Library for Approximate Nearest Neighbors".
Stéfan
_
On Wed, May 2, 2012 at 4:26 PM, Moroney, Catherine M (388D)
wrote:
> Using structured arrays is making my code complex when I try to call the
> vectorized function. If I stick to the original record arrays, what's the
> best way of initializing b from a without doing an row-by-row copy?
What doe
On May 2, 2012, at 5:28 PM, Stéfan van der Walt wrote:
> On Wed, May 2, 2012 at 3:20 PM, Francesc Alted wrote:
>> On 5/2/12 4:07 PM, Stéfan van der Walt wrote:
>> Well, as the OP said, coo_matrix does not support dimensions larger than
>> 2, right?
>
> That's just an implementation detail, I wo
On Wed, May 2, 2012 at 7:25 PM, Aronne Merrelli
wrote:
> In general this is a good suggestion - I was going to mention it
> earlier - but I think for this particular problem it is not better
> than the "brute force" and argmin() NumPy approach. On my laptop, the
> KDTree query is about a factor of
On May 2, 2012, at 3:23 PM,
wrote:
>> A) ?How do I most efficiently construct a record array from a single array?
>> I want to do the following, but it segfaults on me when i try to print b.
>>
>> vtype = [("x", numpy.ndarray)]
>> a = numpy.arange(0, 16).reshape(4,4)
>> b = numpy.recarray((4)
On Wed, May 2, 2012 at 5:27 PM, Kevin Jacobs
wrote:
> On Wed, May 2, 2012 at 5:45 PM, Moroney, Catherine M (388D)
> wrote:
>>
>> Thanks to Perry for some very useful off-list conversation. I realize
>> that
>> I wasn't being clear at all in my earlier description of the problem so
>> here it i
On Wed, May 2, 2012 at 11:26 PM, Francesc Alted wrote:
> On 5/2/12 4:20 PM, Nathaniel Smith wrote:
>> On Wed, May 2, 2012 at 9:53 PM, Francesc Alted wrote:
>>> On 5/2/12 11:16 AM, Wolfgang Kerzendorf wrote:
Hi all,
I'm currently writing a code that needs three dimensional data (for
Chris Ball gmail.com> writes:
>
> Keith Hughitt gmail.com> writes:
>
> > Hi Chris,
> >
> > Try "sudo apt-get build-dep python-numpy" to install the dependencies for
> > building NumPy. I believe it will install all of the optional dependencies
> > as well.
>
> Thanks for that, but I'd alrea
On 5/2/12 5:28 PM, Stéfan van der Walt wrote:
> On Wed, May 2, 2012 at 3:20 PM, Francesc Alted wrote:
>> On 5/2/12 4:07 PM, Stéfan van der Walt wrote:
>> Well, as the OP said, coo_matrix does not support dimensions larger than
>> 2, right?
> That's just an implementation detail, I would imagine--I
On Wed, May 2, 2012 at 3:20 PM, Francesc Alted wrote:
> On 5/2/12 4:07 PM, Stéfan van der Walt wrote:
> Well, as the OP said, coo_matrix does not support dimensions larger than
> 2, right?
That's just an implementation detail, I would imagine--I'm trying to
figure out if there is a new principle
On Wed, May 2, 2012 at 5:45 PM, Moroney, Catherine M (388D) <
catherine.m.moro...@jpl.nasa.gov> wrote:
> Thanks to Perry for some very useful off-list conversation. I realize
> that
> I wasn't being clear at all in my earlier description of the problem so
> here it is
> in a nutshell:
>
> Find t
On 5/2/12 4:20 PM, Nathaniel Smith wrote:
> On Wed, May 2, 2012 at 9:53 PM, Francesc Alted wrote:
>> On 5/2/12 11:16 AM, Wolfgang Kerzendorf wrote:
>>> Hi all,
>>>
>>> I'm currently writing a code that needs three dimensional data (for the
>>> physicists it's dimensions are atom, ion, level). The
On Wed, May 2, 2012 at 2:45 PM, Moroney, Catherine M (388D)
wrote:
> Find the best match in an array t(5000, 7) for a single vector e(7). Now
> scale
> it up so e is (128, 512, 7) and I want to return a (128, 512) array of the
> t-identifiers
> that are the best match for e. "Best match" is de
On 5/2/12 4:07 PM, Stéfan van der Walt wrote:
> Hi Francesc
>
> On Wed, May 2, 2012 at 1:53 PM, Francesc Alted wrote:
>> and add another one for the actual values of the array. For a 3-D
>> sparse array, this looks like:
>>
>> dim0 | dim1 | dim2 | value
>> ==
>> 0 |
David Froger gmail.com> writes:
> > I've been working on setting up a new buildbot for
> > NumPy. Unfortunately, I don't have much time to work on it,
> > so it's slow going!
...
> Hi,
>
> If there are things one can contribute to help the development
> of the buildbot for NumPy, I would be ha
On Wed, May 2, 2012 at 1:06 PM, Moroney, Catherine M (388D)
wrote:
> Hello,
>
> Can somebody give me some hints as to how to code up this function
> in pure python, rather than dropping down to Fortran?
>
> I will want to compare a 7-element vector (called "element") to a large list
> of similarl
Thanks to Perry for some very useful off-list conversation. I realize that
I wasn't being clear at all in my earlier description of the problem so here it
is
in a nutshell:
Find the best match in an array t(5000, 7) for a single vector e(7). Now scale
it up so e is (128, 512, 7) and I want to
On Wed, May 2, 2012 at 9:48 AM, Charles R Harris
wrote:
>
>
> On Tue, May 1, 2012 at 11:47 PM, Ralf Gommers
> wrote:
>>
>>
>>
>> On Wed, May 2, 2012 at 1:48 AM, Pauli Virtanen wrote:
>>>
>>> 01.05.2012 21:34, Ralf Gommers kirjoitti:
>>> [clip]
>>> > At this point it's probably good to look again
On Wed, May 2, 2012 at 9:53 PM, Francesc Alted wrote:
> On 5/2/12 11:16 AM, Wolfgang Kerzendorf wrote:
>> Hi all,
>>
>> I'm currently writing a code that needs three dimensional data (for the
>> physicists it's dimensions are atom, ion, level). The problem is that not
>> all combinations do exis
Hi Francesc
On Wed, May 2, 2012 at 1:53 PM, Francesc Alted wrote:
> and add another one for the actual values of the array. For a 3-D
> sparse array, this looks like:
>
> dim0 | dim1 | dim2 | value
> ==
> 0 | 0 | 0 | val0
> 0 | 10 | 100 | val1
> 20 | 5
> I've been working on setting up a new buildbot for NumPy. Unfortunately, I
> don't have much time to work on it, so it's slow going! Right now I am still
> at
> the stage of getting NumPy to pass all its tests on the machines I'm using as
> test slaves. After that, I plan to transfer existing
On 5/2/12 11:16 AM, Wolfgang Kerzendorf wrote:
> Hi all,
>
> I'm currently writing a code that needs three dimensional data (for the
> physicists it's dimensions are atom, ion, level). The problem is that not all
> combinations do exist (a sparse array). Sparse matrices in scipy only deal
> with
On Wed, May 2, 2012 at 9:03 AM, Henry Gomersall wrote:
> Is this some nuance of the way numpy does things? Or am I missing some
> stupid bug in my code?
Try playing with the parameters of the following code:
sz = 1
N = 10
import numpy as np
x = np.arange(sz)
y = x.copy()
x[:-N] = x[N:]
np
On Wed, May 2, 2012 at 11:06 AM, Moroney, Catherine M (388D)
wrote:
> I will want to compare a 7-element vector (called "element") to a large list
> of similarly-dimensioned
> vectors (called "target", and pick out the vector in "target" that is the
> closest to "element"
> (determined by minimi
Hello,
Can somebody give me some hints as to how to code up this function
in pure python, rather than dropping down to Fortran?
I will want to compare a 7-element vector (called "element") to a large list of
similarly-dimensioned
vectors (called "target", and pick out the vector in "target" that
what about numpy.ma? Those are marked array. But they won't be the fastest.
Fred
On Wed, May 2, 2012 at 12:16 PM, Wolfgang Kerzendorf
wrote:
> Hi all,
>
> I'm currently writing a code that needs three dimensional data (for the
> physicists it's dimensions are atom, ion, level). The problem is t
Hi all,
I'm currently writing a code that needs three dimensional data (for the
physicists it's dimensions are atom, ion, level). The problem is that not all
combinations do exist (a sparse array). Sparse matrices in scipy only deal with
two dimensions. The operations that I need to do on those
I'm need to do some shifting of data within an array and am using the
following code:
for p in numpy.arange(array.shape[0], dtype='int64'):
for q in numpy.arange(array.shape[1]):
# A positive shift is towards zero
shift = shift_values[p, q]
if shift >= 0:
On Tue, May 1, 2012 at 11:47 PM, Ralf Gommers
wrote:
>
>
> On Wed, May 2, 2012 at 1:48 AM, Pauli Virtanen wrote:
>
>> 01.05.2012 21:34, Ralf Gommers kirjoitti:
>> [clip]
>> > At this point it's probably good to look again at the problems we want
>> > to solve:
>> > 1. responsive user interface (m
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