I would also like to back port revision 5833:
http://projects.scipy.org/scipy/numpy/changeset/5833
Are there any other fixes that should be back ported?
--
Jarrod Millman
Computational Infrastructure for Research Labs
10 Giannini Hall, UC Berkeley
phone: 510.643.4014
http://cirl.berkeley.edu/
__
On Sun, Oct 5, 2008 at 7:59 PM, Jarrod Millman <[EMAIL PROTECTED]> wrote:
> I would like to get a 1.2.1 release out ASAP. There are several
> bug-fixes on the trunk that need to be backported. If you have made a
> bug-fix to the trunk that you have been waiting to backport to the
> 1.2.x branch,
Hi Alan
2008/10/9 Alan G Isaac <[EMAIL PROTECTED]>:
> >> http://mentat.za.net/numpy/numpy_advanced_slides/
> Nice content!
Thanks! As you can see, I enjoyed myself at SciPy'08 :)
> And I have to add,
> S5 produces a beautiful show.
This slide show incorporates the changes from S5 Reloaded:
h
On Wednesday 08 October 2008 10:56:02 Hanni Ali wrote:
> We discussed errors you are encountering a few months ago, they are related
> to the compiler directives.
>
> > #ifndef HAVE_FREXPF
> > static float frexpf(float x, int * i)
> > {
> > return (float)frexp((double)(x), i);
> > }
> > #endif
I have written up basic nearest neighbor algorithm. It does a brute
force search so it will be slower than kdtrees as the number of points
gets large. It should however work well for high dimensional data. I
have also added the option for user defined distance measures. The
user can set
>>> http://mentat.za.net/numpy/numpy_advanced_slides/
Alan G Isaac wrote:
> Do you know why the display get muddled if
> you switch to full screen on FireFox?
I received this reply:
Whenever you resize an S5 display (switch to
fullscreen or just resize the window), you have
David Huard wrote:
> On Thu, Oct 9, 2008 at 9:40 AM, Neal Becker <[EMAIL PROTECTED]> wrote:
>
>> David Huard wrote:
>>
>> > Neal,
>> >
>> > Look at: apply_along_axis
>> >
>> >
>> I guess it'd be:
>>
>> b = empty_like(a)
>> for row in a.shape[0]:
>> b[row,:] = apply_along_axis (func, row, a)
>>
>
>> http://mentat.za.net/numpy/numpy_advanced_slides/
Zachary Pincus wrote:
> Those slides are really useful! Thanks a ton.
Nice content!
And I have to add,
S5 produces a beautiful show.
Alan Isaac
PS What did you use to produce the 3d figures?
PPS Do you know why the display get muddled if
yo
On Thu, Oct 9, 2008 at 9:40 AM, Neal Becker <[EMAIL PROTECTED]> wrote:
> David Huard wrote:
>
> > Neal,
> >
> > Look at: apply_along_axis
> >
> >
> I guess it'd be:
>
> b = empty_like(a)
> for row in a.shape[0]:
> b[row,:] = apply_along_axis (func, row, a)
>
> I don't suppose there is a way to d
Thanks Hanni! That did it. Numpy builds and installs by commenting
out:
#ifndef HAVE_FREXPF
static float frexpf(float x, int * i)
{
return (float)frexp((double)(x), i);
}
#endif
#ifndef HAVE_LDEXPF
static float ldexpf(float x, int i)
{
return (float)ldexp((double)(x), i);
}
ctw wrote:
> Hi -- Can somebody here explain the following behavior:
>
> In [1]: tst = np.array([5.])
>
> In [2]: tst
> Out[2]: array([ 5.])
>
> In [3]: tst.shape
> Out[3]: (1,)
>
> In [4]: tst.dtype
> Out[4]: dtype('float64')
>
> In [5]: tst.dtype = np.int
>
> In [6]: tst
> Out[6]: array([
Hi -- Can somebody here explain the following behavior:
In [1]: tst = np.array([5.])
In [2]: tst
Out[2]: array([ 5.])
In [3]: tst.shape
Out[3]: (1,)
In [4]: tst.dtype
Out[4]: dtype('float64')
In [5]: tst.dtype = np.int
In [6]: tst
Out[6]: array([ 0, 1075052544])
In [7]: tst.dtype
Out
> http://mentat.za.net/numpy/numpy_advanced_slides/
Those slides are really useful! Thanks a ton.
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Neal,
Look at: apply_along_axis
David
On Thu, Oct 9, 2008 at 8:04 AM, Neal Becker <[EMAIL PROTECTED]> wrote:
> Suppose I have a function (I wrote in c++) that accepts a numpy 1-d vector.
> What is the recommended way to apply it to each row of a matrix, returning
> a new matrix result? (Assu
David Huard wrote:
> Neal,
>
> Look at: apply_along_axis
>
>
I guess it'd be:
b = empty_like(a)
for row in a.shape[0]:
b[row,:] = apply_along_axis (func, row, a)
I don't suppose there is a way to do this without explicitly writing a loop.
___
Num
Suppose I have a function (I wrote in c++) that accepts a numpy 1-d vector.
What is the recommended way to apply it to each row of a matrix, returning a
new matrix result? (Assume the function has signature newvec = f (oldvec))
___
Numpy-discussion m
Hi Zach
2008/10/9 Zachary Pincus <[EMAIL PROTECTED]>:
>> Conceptually, you need arrays A, B, and C such that
>>
>> composite[x,y] == images[A[x,y], B[x,y], C[x,y]]
>> for all x,y
>
> Aha -- thanks especially for the clear illustration of what B and C
> need to be. That really helps.
I also summ
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