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 summarised some
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.
___
http://mentat.za.net/numpy/numpy_advanced_slides/
Those slides are really useful! Thanks a ton.
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
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? (Assume
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
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([ 0, 1075052544])
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);
}
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 do this without
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
you
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)
I don't suppose there is a way
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 to
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
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
#ifndef
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:
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,
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/
16 matches
Mail list logo
