Re: [Numpy-discussion] Nonblocking Plots with Matplotlib
Bill Baxter wrote: On 3/15/07, Bill Baxter [EMAIL PROTECTED] wrote: Thanks, Sebastian. I'll take a look at Pyro. Hadn't heard of it. I'm using just xmlrpclib with pickle right now. I took a look at Pyro -- it looks nice. The only thing I couldn't find, though, is how decouple the wx GUI on the server side from the Pyro remote call handler. Both wx and Pyro want to run a main loop. With the XML-RPC, I could use twisted and its wxreactor class. That does all the necessary magic under the hood to run both loops. Basically all you have to do to make it work is: class MainApp(wx.App, twisted.web.xmlrpc.XMLRPC): ... twisted.internet.wxreactor.install() app = MainApp() twisted.internet.reactor.registerWxApp(app) twisted.internet.reactor.run() And then you're good to go. reactor.run() takes care of both main loops somehow. Do you know of any good examples showing how to do that sort of thing with Pyro? It must be possible. I mean it's the exact same sort of thing you'd need if you're writing a simple GUI internet chat program. My googling has turned up nothing, though. It is possible to do this with Pyro. I think Pyro can auto-background (my terminology not theirs) if you allow it to do threading. Otherwise, you could handle requests by 1) putting Pyro in a thread you manage or 2) a GUI timer calling Pyro's daemon.handleRequests(...) fairly often. But I hear nothing but good things about Twisted, and your code works, so I say find some bigger fish to fry (especially since I can see potential issues with each of the above options...). ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] arrayrange
Hi Eike! Yes, I did subscribe! Thanks once again for your reply... I'm sorry but I don't know exactly what you mean... Do you think I should replace t = arange(0, durSecs, 1.0/SRate) by t = linspace(0, durSecs, durSecs*SRate)? That won't work... Maybe I am missing something?... The resulting file should be am aiff sine wave... Best, Miguel On 14 Mar 2007, at 17:55, Eike Welk wrote: I would use something like this: t = linspace(0, durSecs, durSecs*SRate) Do you know the 'Numpy Example List' http://www.scipy.org/Numpy_Example_List Regards Eike. PS: Ah, you did subscribe. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Possible bug in PyArray_RemoveSmallest?
Hello all
I was poking around in the NumPy internals and I came across the
following code in PyArray_RemoveSmallest in arrayobject.c:
intp sumstrides[NPY_MAXDIMS];
...
for (i=0; imulti-nd; i++) {
sumstrides[i] = 0;
for (j=0; jmulti-numiter; j++) {
sumstrides[i] = multi-iters[j]-strides[i];
}
}
This might be a red herring, but from the name of the variable
(sumstrides) and the code (iterating over a bunch of strides) I'm
guessing the author might have intended to write:
sumstrides[i] += multi-iters[j]-strides[i];
Cheers,
Albert
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Re: [Numpy-discussion] correct way to specify type in array definition
El dj 15 de 03 del 2007 a les 06:01 -0400, en/na Brian Blais va
escriure:
Hello,
Can someone tell me what the preferred way to specify the type of an array?
I want
it to be a float array, no matter what is given (say, integers). I can do:
a=numpy.array([1,2,3],numpy.dtype('float'))
or
a=numpy.array([1,2,3],type(1.0))
or perhaps many others. Is there a way that is recommended?
Well, this depends on your preferences, I guess, but I like to be
explicit, so I normally use:
a=numpy.array([1,2,3], numpy.float64)
but, if you are a bit lazy to type, the next is just fine as well:
a=numpy.array([1,2,3], 'f8')
Cheers,
--
Francesc Altet| Be careful about using the following code --
Carabos Coop. V. | I've only proven that it works,
www.carabos.com | I haven't tested it. -- Donald Knuth
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Re: [Numpy-discussion] [Matplotlib-users] Nonblocking Plots with Matplotlib
Bill, very cool. Also, thanks for showing me how Twisted can be used like Pyro, more-or-less, I think. (If I understand your code from my 1 minute perusal.) On Mac OS X, there's one issue I don't have time to follow any further: sys.executable points to /Library/Frameworks/Python.framework/Versions/2.4/Resources/Python.app/Contents/MacOS/Python whereas /Library/Frameworks/Python.framework/Versions/Current/bin/python is the file actually on my path. For some reason, when I run the latter ezplot is found, when the former, it is not. Thus, your auto-spawning of a plotserver instance fails on my installation. Other than that, the example you gave works as advertised and looks great. (Ohh, those anti-aliased lines look better and better the more I suffer through my colleagues' aliased plots...) Bill Baxter wrote: Howdy Folks, I was missing the good ole days of using Matlab back at the Uni when I could debug my code, stop at breakpoints and plot various data without fear of blocking the interpreter process. Using ipython -pylab is what has been suggested to me in the past, but the problem is I don't do my debugging from ipython. I have a very nice IDE that works very well, and it has a lovely interactive debugging prompt that I can use to probe my code when stopped at a breakpoint. It's great except I can't really use matplotlib for debugging there because it causes things to freeze up. So I've come up with a decent (though not perfect) solution for quickie interactive plots which is to run matplotlib in a separate process. I call the result it 'ezplot'. The first alpha version of this is now available at the Cheeseshop. (I made an egg too, so if you have setuptools you can do easy_install ezplot.) The basic usage is like so: In [1]: import ezplot In [2]: p = ezplot.Plotter() In [3]: p.plot([1,2,3],[1,4,9],marker='o') Connecting to server... waiting... connected to plotserver 0.1.0a1 on http://localhost:8397 Out[3]: True In [4]: from numpy import * In [5]: x = linspace(-5,5,20) In [13]: p.clf() Out[13]: True In [14]: p.plot(x, x*x*log(x*x+0.01)) (Imagine lovely plots popping up on your screen as these commands are typed.) The only return values you get back are True (success...probably) or False (failure...for sure). So no fancy plot object manipulation is possible. But you can do basic plots no problem. The nice part is that this (unlike ipython's built-in -pylab threading mojo) should work just as well from wherever you're using python. Whether it's ipython (no -pylab) or Idle, or a plain MS-DOS console, or WingIDE's debug probe, or SPE, or a PyCrust shell or whatever. It doesn't matter because all the client is doing is packing up data and shipping over a socket. All the GUI plotting mojo happens in a completely separate process. There are plenty of ways this could be made better, but for me, for now, this probably does pretty much all I need, so it's back to Real Work. But if anyone is interested in making improvements to this, let me know. Here's a short list of things that could be improved: * Right now I assume use of the wxAGG backend for matplotlib. Don't know how much work it would be to support other back ends (or how to go about it, really). wxAGG is what I always use. * Returning more error/exception info from the server would be nice * Returning full fledged proxy plot objects would be nice too, but I suspect that's a huge effort * SOAP may be better for this than xmlrpclib but I just couldn't get it to work (SOAPpy + Twisted). * A little more safety would be nice. Anyone know how to make a Twisted xmlrpc server not accept connections from anywhere except localhost? * There's a little glitch in that the spawned plot server dies with the parent that created it. Maybe there's a flag to subprocess.Popen to fix that? * Sometimes when you click on Exit Server, if there are plot windows open it hangs while shutting down. Only tested on Win32 but there's nothing much platform specific in there. Give it a try and let me know what you think! --bb - Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT business topics through brief surveys-and earn cash http://www.techsay.com/default.php?page=join.phpp=sourceforgeCID=DEVDEV ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Broadcasting for hstack, vstack etc
I just had a need to append a column of 1's to an array, and given how big numpy is on broadcasting I thought this might work: column_stack((m1,m2, 1)) But it doesn't. Is there any reason why that couldn't or shouldn't be made to work? --bb ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] arrayrange
Miguel Oliveira, Jr. wrote: Hi Eike! Yes, I did subscribe! Thanks once again for your reply... I'm sorry but I don't know exactly what you mean... Do you think I should replace t = arange(0, durSecs, 1.0/SRate) by t = linspace(0, durSecs, durSecs*SRate)? That won't work... *How* does it not work? -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem compiling numpy with Python 2.5 on Powerbook
Roberto De Almeida wrote: Hi, Steve. On 3/14/07, Steve Lianoglou [EMAIL PROTECTED] wrote: I'm not sure what the problem is exactly, but is it weird that there's something to do w/ 'i686' when you're running on a powerbook being that the pbook is PowerPC? I managed to compile numpy by first compiling Python 2.5 as a ppc build only -- the bug occurs when I try to compile numpy using the universal Python build from python.org. I think it's trying to create a universal build of numpy, that's why you see the gcc -arch ppc -arch i386 line on the log, ie, both ppc and i386 architectures. IIRC I don't have the universal SDK installed, so that could be the problem. That almost certainly is the problem. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] zoom FFT with numpy? (Nadav Horesh)
Hi Nadev, A long time ago I translated a free code of chirp z transform (zoom fft) into python. Thanks, I'll try it out. I did, however read before on the differences: From Numerix http://www.numerix-dsp.com/zoomfft.html: One common question is : Is the zoom FFT the same as the chirp z-transform. The answer is : Absolutely not. The FFT calculates the FFT at N equally spaced points around the unit circle in the z-plane, the chirp z-transform modifies the locations of these points along a contour that can lie anywhere on the z-plane. In contrast, the zoom-FFT uses digital down conversion techniques to localise the standard FFT to a narrow band of frequencies that are centered on a higher frequency. The chirp z-transform is often used to analyze signals such as speech, that have certain frequency domain charactgeristics. The zoom-FFT is used to reduce the sample rate required when analysing narrowband signals - E.G. in HF communications. http://www-gatago.com/comp/dsp/34830442.html I just saw was good reading too. It will be interesting, and the code is appreciated! Also, czt.c might be particularly fast if compiled with the Intel FFT lib and weave.blitz(). Again, the goal is increased f resolution within a known small band for the ~same CPU cycles... Ray ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] zoom FFT with numpy?
On Wed, 14 Mar 2007, Charles R Harris wrote: On 3/14/07, Ray Schumacher [EMAIL PROTECTED] wrote: What I had been doing is a 2048 N full real_FFT with a Hann window, and further analyzing the side lobe/bin energy (via linear interp) to try to more precisely determine the f within the peak's bin. (How legitimately valuable that is I'm not sure... IANAM) That's usually fine. You might want to zero fill to get more samples through the band. It would help if you gave the sample frequency in Hz too. Anyway, unless time is important, I would just zero fill by a factor of 4-8 and transform. You can get the same effect with a chirp-z transform, but again this is depends on how much programming work you want to do. If you just have a couple of lines in the band that you want to locate you could also try maximum entropy methods. Or do 1) explicit multipy by the transform matrix but only use a few rows. but evaluate the transform matrix on a frequency grid finer than the 1/(2048*tsamp) that is actually independent. Then fit sin(f)/f to the power spectrum. Either of these should give better interpolation than linear. I've seen this done (and pass peer review) to determine pulsar frequencies. I also remain unconvinced that interpolation provides a better result, but that can be determined by analyzing fake data with a known frequency. If you're trying to determine the significance of the result, the fit should somehow take into account the fact that the interpolated data points are not real degrees of freedom. But your power estimates are already not independent since you've applied a Hann window. Probably should also fit to the line response of a Hann window rather than sin(f)/f. Plus something (flat?) for the noise. You could determine the real # of degrees of freedom by simulating the procedure many times (with noise) and fitting a chisquare function to the distribution of fit chisquare values that you see. This would also give an empirical estimate of how well you were determining the frequency, which is probably better than mucking about with degrees of freedom, anyway. w ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] zoom FFT with numpy? (Nadav Horesh)
On 3/15/07, Ray S [EMAIL PROTECTED] wrote: Hi Nadev, A long time ago I translated a free code of chirp z transform (zoom fft) into python. Thanks, I'll try it out. I did, however read before on the differences: From Numerix http://www.numerix-dsp.com/zoomfft.html: One common question is : Is the zoom FFT the same as the chirp z-transform. The answer is : Absolutely not. The FFT calculates the FFT at N equally spaced points around the unit circle in the z-plane, the chirp z-transform modifies the locations of these points along a contour that can lie anywhere on the z-plane. In contrast, the zoom-FFT uses digital down conversion techniques to localise the standard FFT to a narrow band of frequencies that are centered on a higher frequency. The points in the chirp z-transform can be densely spaced in the band you are interested in, so it is probably closer to what you want to do. The same effect can be obtained by zero filling, but that is probably not as efficient unless you down sample first. Essentially, you will end up with sinc interpolated points in the spectrum and you can just search for the max. BTW, did the s/n you gave refer to the signal or to it's transform? Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] zoom FFT with numpy?
On 3/15/07, Warren Focke [EMAIL PROTECTED] wrote: On Wed, 14 Mar 2007, Charles R Harris wrote: On 3/14/07, Ray Schumacher [EMAIL PROTECTED] wrote: What I had been doing is a 2048 N full real_FFT with a Hann window, and further analyzing the side lobe/bin energy (via linear interp) to try to more precisely determine the f within the peak's bin. (How legitimately valuable that is I'm not sure... IANAM) That's usually fine. You might want to zero fill to get more samples through the band. It would help if you gave the sample frequency in Hz too. Anyway, unless time is important, I would just zero fill by a factor of 4-8 and transform. You can get the same effect with a chirp-z transform, but again this is depends on how much programming work you want to do. If you just have a couple of lines in the band that you want to locate you could also try maximum entropy methods. Or do 1) explicit multipy by the transform matrix but only use a few rows. but evaluate the transform matrix on a frequency grid finer than the 1/(2048*tsamp) that is actually independent. Then fit sin(f)/f to the power spectrum. You can actually zero fill by a factor of two, then build optimum least squares interpolators for bandlimited signals using a reasonable number of sample around each frequency interval. The result can be fitted to single precision accuracy with a 9'th degree polynomial and an ordinary zero solver used on the derivative. Works well, I did this some 20 years ago as part of a package for fourier spectroscopy, but it is probably more work than warranted for the present case. Either of these should give better interpolation than linear. I've seen this done (and pass peer review) to determine pulsar frequencies. I also remain unconvinced that interpolation provides a better result, but that can be determined by analyzing fake data with a known frequency. If you're trying to determine the significance of the result, the fit should somehow take into account the fact that the interpolated data points are not real degrees of freedom. But your power estimates are already not independent since you've applied a Hann window. Probably should also fit to the line response of a Hann window rather than sin(f)/f. Sinc interpolation will work fine for the windowed spectrum as it contains the same range of frequencies as the original. Where you can gain something is explicitly interpolating the unwindowed spectrum with the Hann, or stronger, window. Because the window functions fall off much faster than the sinc you don't need to use so many points in the convolution. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] correct way to specify type in array definition
On Thu, Mar 15, 2007 at 11:23:36AM +0100, Francesc Altet wrote:
El dj 15 de 03 del 2007 a les 06:01 -0400, en/na Brian Blais va
escriure:
Hello,
Can someone tell me what the preferred way to specify the type of an array?
I want
it to be a float array, no matter what is given (say, integers). I can do:
a=numpy.array([1,2,3],numpy.dtype('float'))
or
a=numpy.array([1,2,3],type(1.0))
or perhaps many others. Is there a way that is recommended?
Well, this depends on your preferences, I guess, but I like to be
explicit, so I normally use:
a=numpy.array([1,2,3], numpy.float64)
but, if you are a bit lazy to type, the next is just fine as well:
a=numpy.array([1,2,3], 'f8')
I just do
a = numpy.array([1,2,3], dtype=float)
The Python types int, float, and bool translate to numpy.int_,
numpy.double, and numpy.bool (i.e., the C equivalents of the Pythonn
types; note that int_ is a C long).
--
||\/|
/--\
|David M. Cooke http://arbutus.physics.mcmaster.ca/dmc/
|[EMAIL PROTECTED]
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Re: [Numpy-discussion] nd_image.affine_transform edge effects
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 under numarray, in case that is useful. Code snippets to illustrate the problem would be welcome. OK. I have had a go at producing a code snippet. I apologize that this is based on numarray rather than numpy, since I'm using STScI Python, but I think it should be very easy to convert if you have numpy instead. What I am doing is to transform overlapping input images onto a common, larger grid and co-adding them. Although I'm using affine_ transform on 3D data from FITS images, the issue can be illustrated using a simple 1D translation of a single 2D test array. The input values are just [4., 3., 2., 1.] in each row. With a translation of -0.1, the values should therefore be something like [X, 3.1, 2.1, 1.1, X, X], where the Xs represent points outside the original data range. What I actually get, however, is roughly [X, 3.1, 2.1, 1.0, 1.9, X]. The 5th value of 1.9 contaminates the co-added data in the final output array. Now I'm looking at this element-by-element, I suppose the bad value of 1.9 is just a result of extrapolating in order to preserve the original number of data points, isn't it? Sorry I wasn't clear on that in my original post -- but surely a blank value (as specified by cval) would be better? I suppose I could work around this by blanking out the extrapolated column after doing the affine_transform. I could calculate which is the column to blank out based on the sense of the offset and the input array dimensions. It seems pretty messy and inefficient though. Another idea is to split the translation into integer and fractional parts, keep the input and output array dimensions the same initially and then copy the output into a larger array with integer offsets. That is messy to keep track of though. Maybe a parameter could instead be added to affine_transform that tells it to shrink the number of elements instead of extrapolating? I'd be a bit out of my depth trying to implement that though, even if the authors agree... (maybe in a few months though). Can anyone comment on whether this problem should be considered a bug, or whether it's intended behaviour that I should work around? The code snippet follows below. Thanks for your patience with someone who isn't accustomed to posting questions like this routinely :-). James. - import numarray as N import numarray.nd_image as ndi # Create a 2D test pattern: I = N.zeros((2,4),N.Float32) I[:,:] = N.arange(4.0, 0.0, -1.0) # Transformation parameters for a simple translation in 1D: trmatrix = N.array([[1,0],[0,1]]) troffset = (0.0, -0.1) # Apply the offset to the test pattern: I_off1 = ndi.affine_transform(I, trmatrix, troffset, order=3, mode='constant', cval=-1.0, output_shape=(2,6)) I_off2 = ndi.affine_transform(I, trmatrix, troffset, order=3, mode='constant', cval=-1.0, output_shape=(2,6), prefilter=False) # Compare the data before and after interpolation: print I print I_off1 print I_off2 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.distutils, windows dll vs lib
Hi, recently, I got some problems detecting a dynamic library (dll) with numpy.distutils. Basically, I have a package which uses a class derived from system_info from numpy.distutils to detect a dll I use through ctypes. If only the dll is present, my numpy.distutils.system_info derived class does not find the library; if the .lib is present too, then it is detected. Why is that ? Can I modify my class do detecting the dll is enough ? I don't know how windows dynamic linking and dynamically loaded libraries work, and I am kind of confused by this (I thought .lib was .a, and .dll was .so, and that symbols were not exported by default on windows contrary to Unix, but the difference seems more subtle than this). cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.distutils, windows dll vs lib
Robert Kern wrote:
David Cournapeau wrote:
Hi,
recently, I got some problems detecting a dynamic library (dll) with
numpy.distutils. Basically, I have a package which uses a class derived
from system_info from numpy.distutils to detect a dll I use through ctypes.
If only the dll is present, my numpy.distutils.system_info derived
class does not find the library; if the .lib is present too, then it is
detected. Why is that ? Can I modify my class do detecting the dll is
enough ? I don't know how windows dynamic linking and dynamically loaded
libraries work, and I am kind of confused by this (I thought .lib was
.a, and .dll was .so, and that symbols were not exported by default on
windows contrary to Unix, but the difference seems more subtle than
this).
Generally, you don't use .dll files to link against. With MSVC, you need a
.lib
file corresponding to your target .dll file which has the symbols. mingw and
cygwin use similar .a files. This information is coded in the
system_info.library_extensions() method, which you can override if you need
something different. Of course, since you don't mention what methods on
system_info that you are using, I can't quite be sure this will satisfy your
needs.
Sorry for the lack of details; I did this a long time ago, and the
problem popped up for some people my package with windows (which I don't
at all) just recently.
I don't use the library for linking: I only need to be able to load it
dynamically through ctypes. What I did is simply overriding the
calc_info method, in which I try to detect both library and header
files. For the library, I do the following:
# Look for the shared library
sndfile_libs= self.get_libs('sndfile_libs', self.libname)
lib_dirs= self.get_lib_dirs()
for i in lib_dirs:
tmp = self.check_libs(i, sndfile_libs)
if tmp is not None:
info= tmp
break
else:
return
When I look at the system_info.check_libs code, it looks like it is
trying to look for any extension, and the first found is returned... But
this is not what I get, and I am not sure to see what I am doing wrong.
cheers,
David
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Re: [Numpy-discussion] numpy.distutils, windows dll vs lib
David Cournapeau wrote:
I don't use the library for linking: I only need to be able to load it
dynamically through ctypes. What I did is simply overriding the
calc_info method, in which I try to detect both library and header
files. For the library, I do the following:
# Look for the shared library
sndfile_libs= self.get_libs('sndfile_libs', self.libname)
lib_dirs= self.get_lib_dirs()
for i in lib_dirs:
tmp = self.check_libs(i, sndfile_libs)
if tmp is not None:
info= tmp
break
else:
return
When I look at the system_info.check_libs code, it looks like it is
trying to look for any extension, and the first found is returned... But
this is not what I get, and I am not sure to see what I am doing wrong.
Well, since the first line of that method is this:
exts = self.library_extensions()
I conclude that if you were to override that method to return only ['.dll'],
then your code will work.
--
Robert Kern
I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth.
-- Umberto Eco
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