On Tue, 10 Nov 2009 16:07:32 -0800, Christopher Barker
wrote:
> Hi all,
>
> I have a bunch of points in 2-d space, and I need to find out which
> pairs of points are within a certain distance of one-another (regular
> old Euclidean norm).
How big is your set of points?
>
> scipy.spatial.KDTr
Robert Kern wrote:
>
> No. The APSL is not DFSG-free.
>
It was too good to be true, I guess.
>
> http://c-algorithms.sourceforge.net/
>
Thanks for the link,
David
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On Wed, Nov 11, 2009 at 01:15, David Cournapeau
wrote:
> - The Apple Core Foundation (I have to check the Apple license is
> ok).
No. The APSL is not DFSG-free.
> It is more complex, but is designed with objective-C in mind,
> meaning integration with the C python API may be easier (both Obj
Charles R Harris wrote:
>
> I think Python lists are basically just expanding arrays and pointers
> are cheap. Where you might lose is in creating python objects to put
> in the list and not having ufuncs and the rest of the numpy machinery.
> If you don't need the machinery, lists are probably not
On Tue, Nov 10, 2009 at 10:51 PM, Christopher Barker
wrote:
> Anne Archibald wrote:
> > 2009/11/10 Christopher Barker :
>
> >> I have a bunch of points in 2-d space, and I need to find out which
> >> pairs of points are within a certain distance of one-another (regular
> >> old Euclidean norm).
>
Anne Archibald wrote:
> 2009/11/10 Christopher Barker :
>> I have a bunch of points in 2-d space, and I need to find out which
>> pairs of points are within a certain distance of one-another (regular
>> old Euclidean norm).
>
> This is an eminently reasonable thing to want, and KDTree should
> su
Chris wrote:
> I am building Numpy on OSX 10.6 using a recent update
> from SVN (r7726). Though I was able to build the package
> successfully, the resulting package generates an ImportError:
>
> import umath
> ImportError: dlopen(/Library/Python/2.6/site-packages/
> numpy-1.4.0.dev7726-py2.6-m
I am building Numpy on OSX 10.6 using a recent update
from SVN (r7726). Though I was able to build the package
successfully, the resulting package generates an ImportError:
import umath
ImportError: dlopen(/Library/Python/2.6/site-packages/
numpy-1.4.0.dev7726-py2.6-macosx-10.6-
universal.egg/
On Tue, Nov 10, 2009 at 7:48 PM, Anne Archibald
wrote:
> 2009/11/10 Christopher Barker :
>> Hi all,
>>
>> I have a bunch of points in 2-d space, and I need to find out which
>> pairs of points are within a certain distance of one-another (regular
>> old Euclidean norm).
>
> This is an eminently re
Also, is it not returning distances between points and themselves? Or am I
misinterpreting it?
DG
On Tue, Nov 10, 2009 at 5:17 PM, Christopher Barker
wrote:
> James Bergstra wrote:
> > In some cases a brute-force approach is also good.
>
> true.
>
> > If r is a matrix of shape Nx2:
> >
> > (r*r
On Tue, Nov 10, 2009 at 8:17 PM, Christopher Barker
wrote:
> James Bergstra wrote:
>> In some cases a brute-force approach is also good.
>
> true.
>
>> If r is a matrix of shape Nx2:
>>
>> (r*r).sum(axis=1) -2 * numpy.dot(r, r.T) +
>> (r*r).sum(axis=1).reshape((r.shape[0], 1)) < thresh**2
>>
>> It
James Bergstra wrote:
> In some cases a brute-force approach is also good.
true.
> If r is a matrix of shape Nx2:
>
> (r*r).sum(axis=1) -2 * numpy.dot(r, r.T) +
> (r*r).sum(axis=1).reshape((r.shape[0], 1)) < thresh**2
>
> It's brute force, but it takes advantage of fast matrix multiplication.
On Tue, Nov 10, 2009 at 7:07 PM, Christopher Barker
wrote:
> Hi all,
>
> I have a bunch of points in 2-d space, and I need to find out which
> pairs of points are within a certain distance of one-another (regular
> old Euclidean norm).
>
> scipy.spatial.KDTree.query_ball_tree() seems like it's bui
2009/11/10 Christopher Barker :
> Hi all,
>
> I have a bunch of points in 2-d space, and I need to find out which
> pairs of points are within a certain distance of one-another (regular
> old Euclidean norm).
This is an eminently reasonable thing to want, and KDTree should
support it. Unfortunatel
Hi all,
I have a bunch of points in 2-d space, and I need to find out which
pairs of points are within a certain distance of one-another (regular
old Euclidean norm).
scipy.spatial.KDTree.query_ball_tree() seems like it's built for this.
However, I'm a bit confused. The first argument is a kdt
On Wed, Nov 11, 2009 at 6:18 AM, Michael Droettboom wrote:
> I don't know if your 'long double' detection code is complete yet, but I
> thought I'd share the current build output on one of our Solaris
> machines. It looks like it may just be a typo difference between
> 'IEEE_QUAD_BE' in long_doub
I don't know if your 'long double' detection code is complete yet, but I
thought I'd share the current build output on one of our Solaris
machines. It looks like it may just be a typo difference between
'IEEE_QUAD_BE' in long_double_representation() and 'IEEE_QUAD_16B_BE' in
setup.py, but I wa
Hello!
On Tue, Nov 10, 2009 at 2:23 PM, Pierre GM wrote:
>
> On Nov 10, 2009, at 1:09 PM, Darryl Wallace wrote:
>
> > Hello again,
> >
> > The best way so far that's come to my attention is to use:
> >
> > numpy.ma.masked_object
>
> Will only work for masking one specific string, as you've notic
On Nov 10, 2009, at 1:09 PM, Darryl Wallace wrote:
> Hello again,
>
> The best way so far that's come to my attention is to use:
>
> numpy.ma.masked_object
Will only work for masking one specific string, as you've noticed.
>
> Can anyone help me so that all strings are found in the array with
Thanks for the help,
I'll test out this simple example.
On Tue, Nov 10, 2009 at 2:28 PM, Keith Goodman wrote:
> On Tue, Nov 10, 2009 at 11:14 AM, Keith Goodman
> wrote:
> > On Tue, Nov 10, 2009 at 10:53 AM, Darryl Wallace
> > wrote:
> >> I currently do as you suggested. But when the dataset
On Tue, Nov 10, 2009 at 11:28 AM, Keith Goodman wrote:
> On Tue, Nov 10, 2009 at 11:14 AM, Keith Goodman wrote:
>> On Tue, Nov 10, 2009 at 10:53 AM, Darryl Wallace
>> wrote:
>>> I currently do as you suggested. But when the dataset size becomes large,
>>> it gets to be quite slow due to the ove
On Tue, Nov 10, 2009 at 11:14 AM, Keith Goodman wrote:
> On Tue, Nov 10, 2009 at 10:53 AM, Darryl Wallace
> wrote:
>> I currently do as you suggested. But when the dataset size becomes large,
>> it gets to be quite slow due to the overhead of python looping.
>
> Are you using a for loop? Is so,
Hello,
On Tue, Nov 10, 2009 at 1:32 PM, Gökhan Sever wrote:
> On Tue, Nov 10, 2009 at 12:09 PM, Darryl Wallace
> wrote:
> > Hello again,
> > The best way so far that's come to my attention is to use:
> > numpy.ma.masked_object
> > The problem with this is that it's looking for a specific instan
On Tue, Nov 10, 2009 at 12:09 PM, Darryl Wallace
wrote:
> Hello again,
> The best way so far that's come to my attention is to use:
> numpy.ma.masked_object
> The problem with this is that it's looking for a specific instance of an
> object. So if the user had some elements of their array that we
Hello again,
The best way so far that's come to my attention is to use:
numpy.ma.masked_object
The problem with this is that it's looking for a specific instance of an
object. So if the user had some elements of their array that were, for
example, "randomString" , then it would not be picked up
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