Just tried on a 32bit workstation (both CPU and OS): I get
an error, as before, using python2.5:
---
a.py:5: DeprecationWarning: struct integer overflow masking is deprecated
b=struct.pack(10H,*a)
Traceback (most recent call last):
File a.py, line 5, in module
b=struct.pack(10H,*a)
File
Alan G Isaac wrote:
I never got a response to this:
URL:http://projects.scipy.org/pipermail/scipy-dev/2008-February/008424.html
(Two different types claim to be numpy.int32.)
On Mon, 03 Mar 2008, Travis E. Oliphant apparently wrote:
It's not a bug :-) There are two c-level types that
Damian Eads wrote:
One used -mfpmath=sse, and the other, -mfpmath=387.
Keeping them both
the same cleared the discrepancy.
Oh yes! I think you got it...
On 3/3/08, Christopher Barker [EMAIL PROTECTED] wrote:
Was it really a significant difference, or just noticeable? I hope
not, that
Lisandro Dalcin wrote:
And yes, in
my case the cummulative differences leaded to different iteration
counts in a matrix-free Newton-Krylov method. Of course, the final
answer was as as accurate as the tolerances for the nonlinear solver.
OK, so significant differences in iteration counts, but
Thank you for the input!
It sounds like Fourier methods will be fastest, by design, for sample
counts of hundreds to thousands.
I currently do steps like:
Im1 = get_stream_array_data()
Im2 = load_template_array_data(fh2)
##note: len(im1)==len(im2)
Ffft_im1=fftpack.rfft(Im1)
At 03:28 PM 3/3/2008, Ann wrote:
Sounds familiar. If you have a good signal-to-noise ratio, you can get
subpixel accuracy by oversampling the irfft, or better but slower, by
using numerical optimization to refine the peak you found with argmax.
the S/N here is poor, and high data rates work
All,
Let a b be two ndarrays of the same shape. I'm trying to find the elements
of b that correspond to the minima of a along an arbitrary axis.
The problem is trivial when axis=None or when a.ndim=2, but I'm getting
confused with higher dimensions: I came to the following solution that looks
On 04/03/2008, Pierre GM [EMAIL PROTECTED] wrote:
All,
Let a b be two ndarrays of the same shape. I'm trying to find the elements
of b that correspond to the minima of a along an arbitrary axis.
The problem is trivial when axis=None or when a.ndim=2, but I'm getting
confused with higher
Anne,
Thanks a lot for your suggestion. Something like
if axis is None:
return b.flat[a.argmin()]
else:
return numpy.choose(a.argmin(axis),numpy.rollaxis(b,axis,0))
seems to do the trick fairly nicely indeed. The other solutions you suggested
would require too much ad hoc adaptation.
On 04/03/2008, Pierre GM [EMAIL PROTECTED] wrote:
Anne,
Thanks a lot for your suggestion. Something like
if axis is None:
return b.flat[a.argmin()]
else:
return numpy.choose(a.argmin(axis),numpy.rollaxis(b,axis,0))
seems to do the trick fairly nicely indeed. The other
Anne,
I should have provided the link before, but this is very useful for
answering this kind of question:
http://www.scipy.org/Numpy_Functions_by_Category
Great link indeed, that complements well the example list:
http://www.scipy.org/Numpy_Example_List
Thanks again !
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