Mailman 3 July 2011 - SciPy-User

Gaussian mixture models with censored data
by Giovanni Luca Ciampaglia 26 Jul '11

26 Jul '11

Hello everybody, Before I delve into implementing it myself, is there any Python implementation of the EM algorithm used for fitting Gaussian mixtures that also handles (right-)censored observations? In particular I need to fit univariate data, so something like this: http://dx.doi.org/10.1080/00949659208811452 Best, -- Giovanni Luca Ciampaglia Ph.D. Candidate Faculty of Informatics University of Lugano Web: http://www.inf.usi.ch/phd/ciampaglia/ Bertastraße 36 ∙ 8003 Zürich ∙ Switzerland

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f2py
by collinstocks＠gmail.com 25 Jul '11

25 Jul '11

Lists (scipy-dev and scipy-user), If anybody here is at least somewhat familiar with f2py, could you please review and comment on that section of my pull request: https://github.com/ scipy/scipy/pull/44 Specifically, please take a look at https://github.com/scipy/scipy/pull/44/files#diff-1 Thanks in advance. Collin

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trusting splprep
by elliot 25 Jul '11

25 Jul '11

backstory: I am working on an optical raytracing program written in python. Currently, I am reworking the face that uses a spline approximation of a profile. Specifically, moving from a second order to third order. The evaluating of the spline is done in c code that I intend to write, and so in exploring what exactly the output of splprep means, and how to construct the curve in question from tck, I discovered that I need to know how to use splprep better. question: How can I be sure that my spline approximation is sufficiently accurate? In ray tracing, a slight inaccuracy in the derivative of the interpolation could have a large effect. My use of splprep is awkward, so i am looking for some advice. for instance, just trying a simple example: ##### >>>x = range(0,6) >>>y = [c**3 for c in x] >>>tck,uout = scipy.interpolate.splprep([x,y],s=.000005,k=3) >>>t = numpy.linspace(0,1,6) #evenly spaced parameters, should correspond to x >>> xy = scipy.interpolate.splev(t,tck) >>> print xy[0] #should be same as x array([ 0.10119217, 2.81103321, 3.959404 , 3.90686522, 3.85308141, 4.99999962] # !! x went backwards as parameter progressed: graph is not a function >>> inty = [c**3 for c in xy[0]] #differance between interpolated y's and cubes of interpolated x's >>> numpy.array(xy[1])-numpy.array(inty) array([ -6.36532821e-04, 2.50019069e+00, -1.25009170e+01, 1.53618385e+01, 4.31465996e+01, 2.89434607e-05]) #### Now, this is pretty large error considering that this is a cubic spline on a simple cubic function. This should be pretty accurate, if not exact, but an order of magnitude off just isn't right. changing the smoothness from .00005 to 5 doesn't make a difference. So, how do I use splprep correctly. And, does anyone have any pointers regarding ensuring a certain level of accuracy in the results, or at least raising an error if the spline is not accurate enough. thanks, Elliot

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Cannot use scipy into new clean install of visual studio / ironpython
by kpr0001＠comcast.net 24 Jul '11

24 Jul '11

Hello, I have a new install of Visual studio 2010, into which I just updated the python tools and iron python's latest release (as of 7/15/2011). I downloaded numpy and scipy, modified the path, and followed all of the troubleshooting instructions on your site, stackoverflow, and enthought.com. The libraries appear in the correct place now, but when I type "import python" it throws errors. Traceback (most recent call last): File "C:\Program Files (x86)\IronPython 2.7\lib\site-packages\numpy\core\multiarray.py", line 0, in <module> SystemError: The type initializer for 'NumpyDotNet.NpyCoreApi' threw an exception. exceptions.SystemError Traceback (most recent call last): File "C:\Program Files (x86)\IronPython 2.7\lib\site-packages\numpy\core\multiarray.py", line 0, in <module> SystemError: The type initializer for 'NumpyDotNet.NpyCoreApi' threw an exception. System info is Microsofr .NET framework Version 4.0.30319 RTMRel running under Windows 7 I am out of ideas. Help is much appreciated. Thanks, Karen

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Re: [SciPy-User] Spline Fitting on Dense and Noisy Points
by Jonathan Stickel 22 Jul '11

22 Jul '11

On 07/22/2011 04:29 PM, scipy-user-request(a)scipy.org wrote: > On Thu, Jul 21, 2011 at 5:51 AM, Chong Yang<flyzzx(a)gmail.com> wrote: >> >> >> Hi, >> >> >> >> Recently I have been working with 1D curve fitting on dense GPS point >> >> cloud representing road segments. For all I know, some roads may be highly >> >> curved and their corresponding x value are not necessary monotonic order. >> >> >> >> I tried splrep. It works very well on simple arcs but fails to give >> >> meaningful result on highly curved data - either because the polynomial >> >> order 'k' is not sufficient to fit the data well enough or the curve is >> >> vertically aligned. For now I get around vertical curves by flipping the >> >> axes and doing splrep with (y, x), but maybe a better approach is possible. >> >> >> >> My guess is splprep. However I am not sure how to properly convert the >> >> noisy data points into their parametric form. Does anyone have an idea on >> >> this or faced a similar problem before? Thanks. >> >> I know I am replying to this thread a bit late, but you might also be interested in smoothing by regularization: http://packages.python.org/scikits.datasmooth/regularsmooth.html Scattered data are OK. However, curves that are vertical or bend back over themselves will present problems, as you have found with splines. HTH, Jonathan

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Spline Fitting on Dense and Noisy Points
by Chong Yang 22 Jul '11

22 Jul '11

Hi, Recently I have been working with 1D curve fitting on dense GPS point cloud representing road segments. For all I know, some roads may be highly curved and their corresponding x value are not necessary monotonic order. I tried splrep. It works very well on simple arcs but fails to give meaningful result on highly curved data - either because the polynomial order 'k' is not sufficient to fit the data well enough or the curve is vertically aligned. For now I get around vertical curves by flipping the axes and doing splrep with (y, x), but maybe a better approach is possible. My guess is splprep. However I am not sure how to properly convert the noisy data points into their parametric form. Does anyone have an idea on this or faced a similar problem before? Thanks. -- Regards, CY

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Looking for a vectorized adaptive quadrature routine
by Keenan Pepper 21 Jul '11

21 Jul '11

I'm looking for a numerical quadrature routine with a combination of specific properties. My integrand function is much more efficient if called in batches (vec_func). That seems to eliminate "quad" and "fixed_quad". Also, my integrand is extremely non-uniform, so the quadrature routine must be adaptive. This eliminates "quadrature" and "romberg" (unless I'm really misunderstanding what "romberg" is), as well as the fixed sample quadrature routines. There seems to be nothing left! Does Scipy really lack a vectorized, adaptive quadrature routine? I'm sure there are other users who would benefit from such a feature. Keenan Pepper

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ANN: NumPy 1.6.1 release
by Ralf Gommers 21 Jul '11

21 Jul '11

Hi, I am pleased to announce the availability of NumPy 1.6.1. This is a bugfix release for the 1.6.x series; the list of fixed bugs is given below. Sources and binaries can be found at http://sourceforge.net/projects/numpy/files/NumPy/1.6.1/ Thanks to anyone who contributed to this release. Enjoy, The NumPy developers Bug fixes for NumPy 1.6.1 ------------------------- #1834 einsum fails for specific shapes #1837 einsum throws nan or freezes python for specific array shapes #1838 object <-> structured type arrays regression #1851 regression for SWIG based code in 1.6.0 #1863 Buggy results when operating on array copied with astype() #1870 Fix corner case of object array assignment #1843 Py3k: fix error with recarray #1885 nditer: Error in detecting double reduction loop #1874 f2py: fix --include_paths bug #1749 Fix ctypes.load_library() #1895/1896 iter: writeonly operands weren't always being buffered correctly

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Blog post about pandas, datarray, data structures in general, and where to from here
by Wes McKinney 21 Jul '11

21 Jul '11

I can't help myself from continuing to kick the hornet's nest: http://news.ycombinator.org/item?id=2790762 but in short after SciPy 2011, I'm very excited to keep the dialog going and keep making progress. - Wes

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check_format in sparse matrix creation
by Gabriel Dulac-Arnold 21 Jul '11

21 Jul '11

Is there a good reason that check_format cannot be disabled when creating sparse arrays in scipy.sparse? If the correct ndarrays are provided in the constructor, a lot of what check_format does (pruning, typecasting to native byteorder) is not necessary, and wastes a lot of time in situations where sparse arrays are being generated in large numbers. Has this been considered / was there a good reason not to allow disabling it? Thanks, Gabe

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