Mailman 3 October 2012 - SciPy-User

scipy.signal bilinear xfrom with prewarping?
by Neal Becker Oct. 24, 2012

Oct. 24, 2012

As explained in: http://www.mathworks.com/help/signal/ref/bilinear.html Is there any way to use scipy.signal to include the 'prewarping' parameter?

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RLS algorithm?
by Neal Becker Oct. 23, 2012

Oct. 23, 2012

Anyone have code for RLS (recursive least squares)? I have one version, but it seems to be rather unstable.

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numpy.histogram is slow
by Chris Weisiger Oct. 23, 2012

Oct. 23, 2012

My use case is displaying camera image data to the user as it is streamed to us; this includes a histogram showing the distribution of intensities in the image. Thus I have a 512x512 array of pixel data (unsigned 16-bit ints) that I need to generate a histogram for. Unfortunately, numpy.histogram takes a significant amount of time -- about 15ms per call. That's over 60% of the cost of showing an image to the user, which means that I can't quite display data as quickly as it comes in. So I'm looking for some faster option. My searches turned up numpy.bincount, which is nice and zippy, but unfortunately omits bins where the total count is 0. This makes sense considering that otherwise it would always generate a length-N array where N is the maximum value in the input, but it doesn't work for my purposes. Are there any better options? -Chris

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Re: [SciPy-User] Error function with complex argument
by Robaula Oct. 21, 2012

Oct. 21, 2012

See also SciPy ufunc wofz, where erf(z) = 1 - exp( -z**2 ) * wofz( i*z ). This coud be used to compare values. Bob H On Oct 21, 2012, at 10:00 AM, scipy-user-request(a)scipy.org wrote: > Send SciPy-User mailing list submissions to > scipy-user(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://mail.scipy.org/mailman/listinfo/scipy-user > or, via email, send a message with subject or body 'help' to > scipy-user-request(a)scipy.org > > You can reach the person managing the list at > scipy-user-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of SciPy-User digest..." > > > Today's Topics: > > 1. Re: error function with complex argument (Pauli Virtanen) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sat, 20 Oct 2012 21:55:01 +0000 (UTC) > From: Pauli Virtanen <pav(a)iki.fi> > Subject: Re: [SciPy-User] error function with complex argument > To: scipy-user(a)scipy.org > Message-ID: <loom.20121020T235319-577(a)post.gmane.org> > Content-Type: text/plain; charset=utf-8 > > Claas H. K?hler <claas.koehler <at> dlr.de> writes: >> I have a question regarding the error function scipy.special.erf: > [clip] > > A new better implementation with both Re/Im parts accurate > (down to some ULPs, I think) is here: > > https://github.com/scipy/scipy/pull/340 > > -- > Pauli Virtanen > > > > > ------------------------------ > > _______________________________________________ > SciPy-User mailing list > SciPy-User(a)scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-user > > > End of SciPy-User Digest, Vol 110, Issue 40 > *******************************************

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error function with complex argument
by "Claas H. Köhler" Oct. 20, 2012

Oct. 20, 2012

Hi list! I have a question regarding the error function scipy.special.erf: Is it intended, that the erf of an imaginary argument yields a non-vanishing real-part? I get e.g. erf(1j)= 1.6504257587975431j erf(5j)= (1+8298273879.8992386j) The first result is what I would expect in accordance with Wolfram alpha. The second result, however, has a real part of unity. As far as I know, the real part of erf should always vanish for purely imaginary numbers. Any support would be appreciated. Regards Claas

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installation error: undefined symbol: omp_in_parallel
by Craig Plaisance Oct. 19, 2012

Oct. 19, 2012

Hi, I'm having a problem getting scipy installed with linking to mkl 11.1. Here is the output and the mkl section from site.cfg. I've played quite a bit with the mkl_libs variable in site.cfg and googled it to death with no success. Any help is much appreciated! Thanks Craig [root@atlas scipy-0.11.0]# python setup.py config --compiler=intelem --fcompiler=intelem Traceback (most recent call last): File "setup.py", line 208, in <module> setup_package() File "setup.py", line 145, in setup_package from numpy.distutils.core import setup File "/usr/local/lib/python2.7/site-packages/numpy/__init__.py", line 137, in <module> import add_newdocs File "/usr/local/lib/python2.7/site-packages/numpy/add_newdocs.py", line 9, in <module> from numpy.lib import add_newdoc File "/usr/local/lib/python2.7/site-packages/numpy/lib/__init__.py", line 13, in <module> from polynomial import * File "/usr/local/lib/python2.7/site-packages/numpy/lib/polynomial.py", line 17, in <module> from numpy.linalg import eigvals, lstsq File "/usr/local/lib/python2.7/site-packages/numpy/linalg/__init__.py", line 48, in <module> from linalg import * File "/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.py", line 23, in <module> from numpy.linalg import lapack_lite ImportError: /share/apps/intel/Compiler/11.1/046/mkl/lib/em64t/libmkl_intel_thread.so: undefined symbol: omp_in_parallel *Here is the mkl section of site.cfg* [mkl] library_dirs = /share/apps/intel/Compiler/11.1/046/mkl/lib/em64t include_dirs = /share/apps/intel/Compiler/11.1/046/mkl/include:/share/apps/intel/Compiler/11.1/046/include lapack_libs = mkl_lapack mkl_libs = mkl_def,mkl_intel_lp64,mkl_intel_thread,mkl_core,guide,iomp5

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Rotated, Anisotropic Gaussian Filtering (Kernel Density Estimation)
by Patrick Marsh Oct. 19, 2012

Oct. 19, 2012

Greetings, I know that people on this list are way smarter than I, so hopefully someone can help me out here. I have a gridded dataset of 1s and 0s with which I'm needing to apply a rotated, anisotropic Gaussian filter to achieve a kernel density estimate. Currently I have some Cython code that I wrote to do this. The code for this can be found here: https://gist.github.com/3900591. In essence, I search through the grid for a value of 1. When a 1 is found, a weighting function is applied to all the surrounding grid points. Their are rotation matrices at work throughout the code to handle the fact the axes of the anisotropic Gaussian kernel can be off-cartesian axes. The code works, and is reasonably efficient for small domains or large grid spacing. However, as grid spacing decreases, the performance takes a substantial hit. Recently I started playing around with Scipy's ndimage. The Gaussian filter routines are exactly what I've been looking for -- as long as my sigma values lie along the cartesian axes. I attempted to rectify this situation by first rotating the underyling data grid so that it lined up with the cartesian grid. (In other words, if the anisotropic Gaussian rotated angle was 45 degrees, I rotated the underlying data by 45 degrees so they aligned.) I then applied the Gaussian filters and finally rotated the data grid back to the original position. This works perfectly…sometimes. The problem with this approach arrises from the rotating of the data grid. Since I can't really afford to lose the sharpness of my underlying data (they are actual observations and need to remain 1s and 0s), I chose to use a spline of order 0. This works some of the time, but, unfortunatley, this does not always conserve the sum of the total number of points. For example: import numpy as np from scipy import ndimage dist = 21 midpoint = np.floor(dist/2) hist = np.zeros((dist, dist), dtype=float) hist[midpoint, midpoint] = 1 hist2 = ndimage.rotate(hist.copy(), 15, order=0, reshape=True, prefilter=False) print(hist.sum(), hist2.sum()) >> 1.0, 0.0 results in hist2 being all 0s, whereas hist has a sum of 1. So, here are my questions: 1. Is there a way to do an image rotation such that grid points aren't lost when using a spline of order 0? 2. Is there an alternative way to achieve what I'm trying to do? 3. Is there a way to speed up the Cython code linked above? Thanks for letting me pick your brain… Patrick --- Patrick Marsh Ph.D. Candidate / Liaison to the HWT School of Meteorology / University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies National Severe Storms Laboratory http://www.patricktmarsh.com

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Announcing Anaconda 1.1
by Travis Oliphant Oct. 19, 2012

Oct. 19, 2012

It would be great to get feedback from folks on this list about how useful the free version of Anaconda (Anaconda CE) is. You can download it directly from this page: http://www.continuum.io/downloads.html * Anaconda 1.1 Announcement Continuum Analytics, Inc. is pleased to announce the release of Anaconda Pro 1.1, which extends Anaconda’s programming capabilities to the desktop. Anaconda Pro now includes an IDE (Spyder <http://spyder-ide.blogspot.com/>) and plotting capabilities (Matplotlib <http://matplotlib.org/>), as well as optimized versions of Numba Pro <https://store.continuum.io/cshop/numbapro>and IOPro <https://store.continuum.io/cshop/iopro>. With these enhancements, AnacondaPro is a complete solution for server-side computation or client-side development. It is equally well-suited for supercomputers or for training in a classroom. Available for Windows, Mac OS X, and Linux, Anaconda is the premiere Python distribution for scientific computing, engineering simulation, and business intelligence & data management. It includes the most popular numerical and scientific libraries used by scientists, engineers, and data analysts, with a single integrated and flexible installer. Continuum Analytics offers Enterprise-level support for Anaconda, covering both its open source libraries as well as the included commercial libraries from Continuum. For more information, to download a trial version of Anaconda Pro, or download the completely free Anaconda CE, click here<https://store.continuum.io/cshop/anaconda> . * * * *Best regards,* * * *-Travis* * * * * * *

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installation error: undefined symbol: omp_in_parallel
by Craig Plaisance Oct. 19, 2012

Oct. 19, 2012

Hi, I'm having a problem getting scipy installed with linking to mkl 11.1. Here is the output and the mkl section from site.cfg. I've played quite a bit with the mkl_libs variable in site.cfg and googled it to death with no success. Any help is much appreciated! Thanks Craig [root@atlas scipy-0.11.0]# python setup.py config --compiler=intelem --fcompiler=intelem Traceback (most recent call last): File "setup.py", line 208, in <module> setup_package() File "setup.py", line 145, in setup_package from numpy.distutils.core import setup File "/usr/local/lib/python2.7/site-packages/numpy/__init__.py", line 137, in <module> import add_newdocs File "/usr/local/lib/python2.7/site-packages/numpy/add_newdocs.py", line 9, in <module> from numpy.lib import add_newdoc File "/usr/local/lib/python2.7/site-packages/numpy/lib/__init__.py", line 13, in <module> from polynomial import * File "/usr/local/lib/python2.7/site-packages/numpy/lib/polynomial.py", line 17, in <module> from numpy.linalg import eigvals, lstsq File "/usr/local/lib/python2.7/site-packages/numpy/linalg/__init__.py", line 48, in <module> from linalg import * File "/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.py", line 23, in <module> from numpy.linalg import lapack_lite ImportError: /share/apps/intel/Compiler/11.1/046/mkl/lib/em64t/libmkl_intel_thread.so: undefined symbol: omp_in_parallel *Here is the mkl section of site.cfg* [mkl] library_dirs = /share/apps/intel/Compiler/11.1/046/mkl/lib/em64t include_dirs = /share/apps/intel/Compiler/11.1/046/mkl/include:/share/apps/intel/Compiler/11.1/046/include lapack_libs = mkl_lapack mkl_libs = mkl_def,mkl_intel_lp64,mkl_intel_thread,mkl_core,guide,iomp5

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planet scipy dead or alive?
by josef.pktd＠gmail.com Oct. 18, 2012

Oct. 18, 2012

Planet scipy hasn't been updating for a month. Josef

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