Hi Chris & Stefan,
I will be around for most of the weekend (as I believe will Perry).
I'm not sure I'll be able to contribute a lot to coding, but if
there's any stuff you want to co-ordinate between STScI and Stefan's
scikit, let me know if I can help. That's probably about the most
useful thing
Hi Shawn,
> I am trying to find 1-D cubic spline function. But I am not able to call
> it. Error message can’t find “ndimage”
Ndimage is a module in SciPy, so you'd need to install that first,
not just NumPy. I'm not really familar with the available 1-D cubic
spline functions, but I think you w
> oops. It is ATLAS. I was able to run with a nonoptimized lapack.
Just to confirm, it also works for me when I use Netlib BLAS instead
of ATLAS.
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> This smells like an ATLAS problem. You should seed a note to Clint
> Whaley (the ATLAS guy). IIRC, ATLAS has some hand coded asm routines and
> it seems that support for these very new processors might be broken.
I believe the machine is a couple of years old, though it's a
fairly high-end wo
> Rather than look for errors in the scaling factors or errors in your code, I
> think that you should try to expand your understanding of the (subtly)
> different
> types of Fourier representations.
I'd strongly recommend "The Fourier Transform and its Applications"
by Bracewell, if that helps.
Thanks everyone. I think I might try using the Netlib BLAS, since
it's a server installation... but please let me know if you'd like
me to troubleshoot this some more (the sooner the easier).
James.
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> Are you using ATLAS? If so, where did you get it and what cpu do you have?
Yes. I have Atlas 3.8.2. I think I got it from
http://math-atlas.sourceforge.net. I also included Lapack 3.1.1
from Netlib when building it from source. This worked on another
machine.
According to /proc/cpuinfo, I have
Thanks, Robert.
> Can you do
>
> numpy.test(verbosity=2)
OK. Here is the line that fails:
check_matvec (numpy.core.tests.test_numeric.TestDot)Floating exception (core
dumped)
> A gdb backtrace would also help.
OK. I'm pretty ignorant about using debuggers, but I did
"gdb python core.23696" a
I have built NumPy 1.1.0 on RedHat Enterprise 3 (Linux 2.4.21
with gcc 3.2.3 and glibc 2.3.2) and Python 2.5.1. When I run
numpy.test() I get a core dump, as follows. I haven't noticed
any special errors during the build. Should I post the entire
terminal output from "python setup.py install"? Mayb
Thanks, Robert and Stefan for your helpful replies. It makes
a big difference to know which problem I need to solve and which
I don't :-).
Unfortunately I'm still getting those undefined symbol errors
for certain maths functions. I tried "python setup.py build_ext
-lm build" and don't have $LDFLAG
Hello,
I'm trying to build Python 2.5.1 on Solaris 9 with the Sun
WorkShop 6 compiler, but it is failing to build the ctypes
extension. Can anyone tell me whether NumPy 1.1 (or 1.04) can
work without ctypes, please? What about SciPy 0.6? Maybe
that's a silly question, but I can't see how to make i
Hi Robert et al.,
> Please do not respond to digest messages. If you want to respond to
> messages, subscribe to receive messages individually. Respond to the
> just messages you are interested in and keep the Subject lines intact.
Just a suggestion, which I hope doesn't annoy anyone :-).
I rece
Hi Martin,
> I was wondering if anyone has thought about accelerating NumPy with a
> GPU. For example nVidia's CUDA SDK provides a feasible way to offload
> vector math onto the very fast SIMD processors available on the GPU.
> Currently GPUs primarily support single precision floats and are n
Hi Anne,
Your reply to Lou raises a naive follow-up question of my own...
> Normally, python's multithreading is effectively cooperative, because
> the interpreter's data structures are all stored under the same lock,
> so only one thread can be executing python bytecode at a time.
> However, man
Hi Stefan,
Sorry for the slow reply to this.
> Thanks for spotting that. When I fix those lines, I see:
>
> [[ 3.901 3.099 2.099 1.1002 1.8998 2.901 ]
> [ 3.901 3.099 2.099 1.1002 1.8998 2.901 ]]
Actually, I think I made a mis
> It looks like the last output value is produced by reflecting the
> input and then interpolating, but presumably then the first value
> should be 3.9, for consistency, not 3.1? Does that make sense?
Aargh. I think I see what's happening now. The input is supposed to
be interpolated and then r
> OK, that was a one-line patch. Please test to see if there are any
> subtle conditions on the border that I may have missed. I know of one
> already, but I'd be glad if you can find any others :)
Thanks, Stefan! That looks much better.
Today I finally had time to figure out the basics of SV
PS... (sorry for all the posts, for anyone who isn't interested...)
> Agreed, it looks like aliasing. Nevertheless, any resampling
> procedure is supposed to deal with this internally, right? Either by
> lowpass filtering (traditional case), or by spline fitting (spline
> case as described by Unse
Hi Stéfan,
> Agreed, but the aliasing effects isn't not the problem here, as it
> should be visible in the input image as well.
It's a bit academic now that Zach seems to have found the answer, but
I don't think this is true. Aliasing is *present* in the input image,
but is simply manifested as
Hi Zach,
> Based on my reading of the two excellent Unser papers (both the one
> that ndimage's spline code is based on, and the one that Travis
> linked to), it seems like a major point of using splines for
> interpolation is *better* behavior in the case of non-band-limited
> data than the
Hi Zach,
> Hmm, this is worrisome. There really shouldn't be ringing on
> continuous-tone images like Lena -- right? (And at no step in an
> image like that should gaussian filtering be necessary if you're
> doing spline interpolation -- also right?)
That's hard to say. Just because it's mainly
Thanks for the explanation, Travis.
It now looks like there are 2 distinct issues getting mixed up here,
however. First, there is the issue of the mirror symmetry of the spline
algorithm affecting the data towards the edges, as described by Peter and
Travis (this is probably also what Anne is refe
Hi Zach,
> further I'm sorry to have caused Peter to be further bothered about
> this non-issue.
Don't worry -- I have let him know that we've probably figured it out.
I hope Stefan agrees.
> I now (hopefully) understand that the spline "pre-filter", while
> explicitly analogous to a traditi
By the way, ringing at sharp edges is an intrinsic feature of higher-
order spline interpolation, right? I believe this kind of interpolant
is really intended for smooth (band-limited) data. I'm not sure why
the pre-filtering makes a difference though; I don't yet understand
well enough what the pr
Hi Zachary,
OK - I sent Peter the URL for your post...
Cheers,
James.
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The people at STScI put me in touch with Peter Verveer, the author of
nd_image. Unfortunately Peter is currently unable to maintain the code
(either in numarray or scipy), but he did send me some comments on the
problem discussed in this thread. Here's what he said:
James.
-
Hi James,
Yes,
Thanks, Stefan.
> In [25]: import numpy as N
> In [26]: x = N.array([[4,3,8,1],[4,3,8,1.]])
> In [27]:
> ndi.geometric_transform(x,shift,output_shape=(2,6),prefilter=False,order=0,cval=-1)
> Out[27]:
> array([[-1., 3., 8., 1., 8., -1.],
> [-1., 3., 8., 1., 8., -1.]])
Your example se
Hi Stefan,
> I'd like to confirm that you see the same results when running your
> script:
>
> [[ 4. 3. 2. 1.]
> [ 4. 3. 2. 1.]]
> [[-1. 3.12520003 2.11439991 1.0171 1.87479997 -1.]
> [-1. 3.12520003 2.11439991 1.0171 1.87479997 -1.]]
> [[-1.
Sorry I accidentally broke this thread in 2 (see thread of March 9).
I tried manually adding the right reference in the mail header to
continue the same thread, but obviously got it wrong (I think because
I replied to the digest email instead of starting a new one). Not
sure whether there is a bet
Hi Stefan,
Thanks for the suggestions!
> Is this related to
>
> http://projects.scipy.org/scipy/scipy/ticket/213
>
> in any way?
As far as I can see, the problems look different, but thanks for
the example of how to document this. I did confirm that your example
exhibits the same behaviour und
Last year I wrote a program that uses the affine_transform()
function in numarray to resample and co-add datacubes with WCS
offsets in 3D. This function makes it relatively easy to handle
N-D offsets and rotations with a smooth interpolant, which is
exactly what I wanted. However, I am finding that
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