Mailman 3 November 2009 - SciPy-User

splprep example
by Nils Wagner Nov. 28, 2009

Nov. 28, 2009

Hi all, I am looking for a cookbook example wrt splprep. Any pointer would be appreciated. Nils

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EPD doesn't run my code
by Ram Rachum Nov. 28, 2009

Nov. 28, 2009

Hello, I have my project GarlicSim which runs in ordinary Python. I tried to run it in EPD, and it's failing, at two distinct points I could identify. (Possibly there are more.) Here's the project code: http://github.com/cool-RR/GarlicSim-for-Python-2.5 I identified one of the points in question. It's about the `win32api` and the `win32process` modules. When I load up the Python shell of my EPD, and try `import win32process`, I get this error dialog: python.exe - Entry Point Not Found The procedure entry point ?PyWinGlobals_Ensure@@YAXXZ could not be located in the dynamic link library pywintypes25.dll. When I try 'import win32api`, I get: python.exe - Entry Point Not Found The procedure entry point ?PyWinObject_AsHANDLE@@YAHPAU_object@@PAPAXH@Z could not be located in the dynamic link library pywintypes25.dll. The second point is that in my wxPython window, the images in the toolbar get cropped. Any idea?

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Re: [SciPy-User] How to find local minimum of 1d histogram
by Kevin Dunn Nov. 28, 2009

Nov. 28, 2009

> On Fri, Nov 27, 2009 at 4:12 PM, David Warde-Farley <dwf(a)cs.toronto.edu> wrote: >> >> On 27-Nov-09, at 2:49 PM, Michael Aye wrote: >> >>> The minimum between the gaussian-like peaks in the histogram shall be >>> used as the masking limit to either show one or the other population >>> of pixel intensities. >>> >>> My idea so far, but I'm not sure, if there is not a more obvious way? >>> * Using interpolate1d to get a spline. >>> * somehow get the coefficients of the spline function. >>> * put them into poly1d >>> * do derivative >>> * get roots of derivative >> >> I had a similar problem, actually, and used >> scipy.ndimage.gaussian_laplace, which will produce a smoothed discrete >> second derivative. The minimum should be pretty easy to locate (it >> will appear as a rather significant maximum peak in the transformed >> curve). > > In a similar direction, I thought of using gaussian_kde to get a > smoothed probability distribution. and look for local minimum. Yet another way: Otsu's method [1], which is a standard algorithm in image processing to segment an image. There are other methods as well. When I've used Otsu's method from real-time image processing (under unpredictable lighting), I use it only to provide a starting value. Then you move left or right along the smoothed histogram (I normally just use a moving average smoother, because other exotic smoothers take too much time and don't improve accuracy that much) until you land up in a minimum. Usually the Otsu initial guess isn't far off, but it can be under some circumstances. [1] http://en.wikipedia.org/wiki/Otsu%27s_method (also see the references at the bottom) HTH, Kevin > Josef > >> >> David >> _______________________________________________ >> SciPy-User mailing list >> SciPy-User(a)scipy.org >> http://mail.scipy.org/mailman/listinfo/scipy-user

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How to find local minimum of 1d histogram
by Michael Aye Nov. 27, 2009

Nov. 27, 2009

Hi! I am still fairly new with scipy, so please forgive me, if this is a simple question. But I couldn't find an example for this. What is the easiest way of finding the local minimum between 2 gaussian-like peaks in a 1d Histogram? Background: Using a histogram on an image to identify 2 populations of intensities. The minimum between the gaussian-like peaks in the histogram shall be used as the masking limit to either show one or the other population of pixel intensities. My idea so far, but I'm not sure, if there is not a more obvious way? * Using interpolate1d to get a spline. * somehow get the coefficients of the spline function. * put them into poly1d * do derivative * get roots of derivative I am ready to go this way, but I wondered if it isn't easier? Best regards and a nice weekend! Michael

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Tool for visualizing queues
by Ram Rachum Nov. 27, 2009

Nov. 27, 2009

I'm working on a simulation in Queueing Theory, and I would like a good tool for visualizing clients standing in queues and servers serving them. I would eventually like the GUI to be interactive as well, so for example the user could drag a client from one queue to another. Any ideas? Ram.

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Mean arrivals per time unit -> Time between consecutive arrivals
by Ram Rachum Nov. 27, 2009

Nov. 27, 2009

Hello, I've just started using scipy/numpy for some queue theory. I have a queue for which the arrival rate is a Poisson distribution. I also have the mean number of arrivals per time unit. I looked around SciPy and I saw I can use scipy.stats.poisson. I was happy that it could make a random variable for number of arrivals per time unit. But I want the time between consecutive arrivals, as a random variable. Does anyone know how I can get that? Thanks, Ram.

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Bivariate Spline Surface Fitting
by Peter Combs Nov. 27, 2009

Nov. 27, 2009

Hi all, I have localization data in 2 color channels that should agree with each other, but in practice, they don't to the level we want. I thought I'd try doing a straight polynomial least squares fit, and while that gives better registration between the two, I'm still not to the level I want. My next thought was a spline fit, so I'm trying to make two least-squares bivariate spline fits: one for taking (x,y) to x', and one for taking (x,y) to y'. import scipy.interpolate as interp ... def makeLSQspline(xl, yl, xr, yr): """docstring for makespline""" xmin = xr.min()-1 xmax = xr.max()+1 ymin = yr.min()-1 ymax = yr.max()+1 n = len(xl) print "xrange: ", xmin, xmax, '\t', "yrange: ", ymin, ymax yknots, xknots = mgrid[ymin:ymax:10j, xmin:xmax:10j] # Makes an 11x11 regular grid of knot locations xspline = interp.LSQBivariateSpline(xr, yr, xl, xknots.flat, yknots.flat) yspline = interp.LSQBivariateSpline(xr, yr, yl, xknots.flat, yknots.flat) def mapping(xr, yr): xl = xspline.ev(xr, yr) yl = yspline.ev(xr, yr) return xl, yl return mapping I have a "Registration Error" function which calculates a mapping for all but the ith point, then plugs that point into the mapping and finds the difference between the predicted value and the known value. For the 2nd order polynomial fit, I get a mean registration error around 7nm, but for the spline fitting using the function above, the mean error is more like 20,000nm. Which (along with all the random junk that gets spit out, such as /Library/Frameworks/Python.framework/Versions/5.1.0/lib/python2.5/site-packages/scipy/interpolate/fitpack2.py:498: UserWarning: Error on entry, no approximation returned. The following conditions must hold: xb<=x[i]<=xe, yb<=y[i]<=ye, w[i]>0, i=0..m-1 If iopt==-1, then xb<tx[kx+1]<tx[kx+2]<...<tx[nx-kx-2]<xe yb<ty[ky+1]<ty[ky+2]<...<ty[ny-ky-2]<ye warnings.warn(message) about 1 copy of this (or something similar) per call of makeLSQSpline: tx= 0.00000000000000 0.00000000000000 0.00000000000000 -264.022095089756 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 28778.4083647834 0.00000000000000 0.00000000000000 0.00000000000000 tx= 0.00000000000000 0.00000000000000 0.00000000000000 -264.022095089756 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 337.266858978529 3468.05790461355 6598.84895024857 9729.63999588358 12860.4310415186 15991.2220871536 19122.0131327886 22252.8041784237 25383.5952240587 28514.3862696937 28778.4083647834 0.00000000000000 0.00000000000000 0.00000000000000 makes me think something isn't quite right. Any guesses what's going on? I have ~3400 data points, roughly evenly spread out over a 28,000nm x 23,000nm grid. On another note, how well is this going to scale up? If I end up collecting hundreds of thousands to low-millions of points, does spline fitting go as O(n^2), or more like O(n)? The error registration function runs as O(n*O(fitting)), and takes around 5 seconds now, so O(N) spline fitting is fine, about an hour run time total, but O(n^2) is very much not. Peter Combs peter.combs(a)berkeley.edu

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[Fwd: Re: ANN: SfePy 2009.4]
by Robert Cimrman Nov. 27, 2009

Nov. 27, 2009

Hi Osman, thanks for trying out the new version! As isfepy works for me, I assume it must be a version issue with mayavi (tested with 3.3.0). What is your mayavi version? If you cannot try 3.3.0, or use it already, could you send me the output of 'gui.scene.print_traits()'? Just put it prior to the offending line... cheers, r. PS: I guess we should discuss this on sfepy-devel only... osman wrote: > On Tue, 2009-11-24 at 13:10 +0100, Robert Cimrman wrote: >> I am pleased to announce release 2009.4 of SfePy. > > Hi Robert, > Thanks for the new version. I put it on my 32 bit ubuntu jaunty. All > tests ran fine without any errors. Then I tried isfepy. I am getting an > error: > In [1]: pb, vec, data = pde_solve('input/poisson.py') > sfepy: left over: ['__builtins__', '__file__', '__name__', '_filename', > '__doc__', '__package__'] > sfepy: reading mesh (database/simple.mesh)... > sfepy: ...done in 0.04 s > sfepy: setting up domain edges... > sfepy: ...done in 0.01 s > sfepy: setting up domain faces... > sfepy: ...done in 0.01 s > sfepy: creating regions... > sfepy: Gamma_Right > sfepy: Omega > sfepy: Gamma_Left > sfepy: ...done in 0.04 s > sfepy: equation "Temperature": > sfepy: dw_laplace.i1.Omega( coef.val, s, t ) = 0 > sfepy: setting up dof connectivities... > sfepy: ...done in 0.00 s > sfepy: describing geometries... > sfepy: ...done in 0.00 s > sfepy: using solvers: > nls: newton > ls: ls > sfepy: matrix shape: (300, 300) > sfepy: assembling matrix graph... > sfepy: ...done in 0.00 s > sfepy: matrix structural nonzeros: 3538 (3.93e-02% fill) > sfepy: updating materials... > sfepy: coef > sfepy: ...done in 0.01 s > sfepy: updating variables... > sfepy: ...done > /usr/lib/python2.6/dist-packages/scipy/linsolve/__init__.py:4: > DeprecationWarning: scipy.linsolve has moved to > scipy.sparse.linalg.dsolve > warn('scipy.linsolve has moved to scipy.sparse.linalg.dsolve', > DeprecationWarning) > sfepy: nls: iter: 0, residual: 1.176265e-01 (rel: 1.000000e+00) > /usr/lib/python2.6/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py:78: DeprecationWarning: scipy.sparse.linalg.dsolve.umfpack will be removed, install scikits.umfpack instead > ' install scikits.umfpack instead', DeprecationWarning ) > sfepy: rezidual: 0.00 [s] > sfepy: solve: 0.01 [s] > sfepy: matrix: 0.00 [s] > sfepy: nls: iter: 1, residual: 9.957055e-17 (rel: 8.464973e-16) > > In [2]: view = Viewer(pb.get_output_name()) > > In [3]: view() > --------------------------------------------------------------------------- > AttributeError Traceback (most recent call > last) > > /home/osman/sfepy-release-2009.4/sfepy/interactive/__init__.py in > <module>() > ----> 1 > 2 > 3 > 4 > 5 > > /home/osman/sfepy-release-2009.4/sfepy/postprocess/viewer.py in > call_mlab(self, scene, show, is_3d, view, roll, layout, scalar_mode, > vector_mode, rel_scaling, clamping, ranges, is_scalar_bar, > rel_text_width, fig_filename, resolution, filter_names, only_names, > step, anti_aliasing) > 555 else: > 556 gui = ViewerGUI(viewer=self) > --> 557 scene = gui.scene.mayavi_scene > 558 > 559 if scene is not self.scene: > > AttributeError: 'MlabSceneModel' object has no attribute 'mayavi_scene' > > > 2009.3 release has no problem with isfepy. > > Best, > Osman

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[OT] Transform (i.e., Fourier, Laplace, etc.) methods in Prob. & Stats.
by David Goldsmith Nov. 26, 2009

Nov. 26, 2009

Are there enough applications of transform methods (by which I mean, Fourier, Laplace, Z, etc.) in probability & statistics for this to be considered its own specialty therein? Any text recommendations on it (even if it's only a chapter dedicated to it)? Thanks, DG

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scipy.linalg import issues on Mac OS X Snow Leopard
by Michael Temkin Nov. 25, 2009

Nov. 25, 2009

I've been having numerous issues getting scipy to work on Mac OX 10.6. I finally got it to build using the instructions from http://blog.hyperjeff.net/?p=160. For some reason the scipy superpack won't work on my machine (and neither would the macports release) so building from source was the best option. Even though the build was finally successful, I am still unable to use the library. The error message I am getting now is: from scipy.linalg import norm, inv File "/Library/Python/2.6/site-packages/scipy/linalg/__init__.py", line 8, in <module> from basic import * File "/Library/Python/2.6/site-packages/scipy/linalg/basic.py", line 17, in <module> from lapack import get_lapack_funcs File "/Library/Python/2.6/site-packages/scipy/linalg/lapack.py", line 17, in <module> from scipy.linalg import flapack ImportError: dlopen(/Library/Python/2.6/site-packages/scipy/linalg/flapack.so, 2): Symbol not found: _f2pywrapdlamch_ Referenced from: /Library/Python/2.6/site-packages/scipy/linalg/flapack.so Expected in: dynamic lookup Everything is where it should be, and as far as I know I am not doing anything non-standard. I'm using python 2.6.4, all the libraries were built with no obvious issues. Does anyone have any ideas as to what could cause this issue? thanks.

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