Mailman 3 February 2011 - NumPy-Discussion

Problems with installation (wrong Python version)
by Dumka Dumka Feb. 23, 2011

Feb. 23, 2011

Dear numpy Community, I try to install numpy on my Windows 7. I take the code from here: http://sourceforge.net/projects/numpy/files/NumPy/ (I click on "Download numpy-1.5.1-win32-superpack-python2.6.exe (5.4 MB)"). After I start the installation, the installer tells me: Python version 2.6 (Found in registry). I also see the link to the "C:\Python26" directory in the installation window. However, I want to use Python2.7 (I had it on my computer together with the Python 2.6). So, I deleted the "C:\Python26" directory an set a new value to the "Path" environment variable to make a link to the newer version of the Python (version 2.7). I also restarted the computer. However, it does not help. When I try to run the numpy installer again I see the same: Python version 2.6 (Found in registry). I cannot understand where the installer find the link to the old version. If I just continue the installation, it fails (and it started to fail after I deleted the "C:\Python26" directory). So, to summarize, numpy installer "thinks" that I have Python 2.6 (and I don't) and then it fails because it is wrong. How can I force the numpy installer to use Python 2.7? Can I? Thank you in advance for any help, Dumka

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Different behavior for astype('str') in numpy 1.5.1 vs 1.6.0?
by Skipper Seabold Feb. 22, 2011

Feb. 22, 2011

I get a lot of errors and failures running our tests with most recent numpy trunk. One in particular (so far) seems to be a bug. In [1]: import numpy as np In [2]: np.__version__ Out[2]: '1.6.0.dev-c50af53' In [3]: tmp_arr = np.array([['black'],['white'],['other']]) In [4]: tmp_arr.astype('str') Out[4]: array([['b'], ['w'], ['o']], dtype='|S1') The old behavior In [1]: import numpy as np In [2]: np.__version__ Out[2]: '1.5.1' In [3]: tmp_arr = np.array([['black'],['white'],['other']]) In [4]: tmp_arr.astype('str') Out[4]: array([['black'], ['white'], ['other']], dtype='|S5') Should I file a ticket? Skipper PS. Is there an incompatibility of numpy 1.5.1 and numpy 1.6.0 trunk for packages that depend on numpy?

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Re: [Numpy-discussion] Numpy 1.5.1 - Mac - with Activestate Python 3
by Sridhar Ratnakumar Feb. 21, 2011

Feb. 21, 2011

Hi, Just wanted to respond to http://comments.gmane.org/gmane.comp.python.numeric.general/42084 *CORTESI: I have installed ActiveState's Python 3 packages on Mac OS X 10.6.6. [...] What can I do to persuade numpy to install? Must I build it from sourceto get it to use Python 3? RALF: [...] there is no [NumPy] binary for Python 3 at the moment. But unless you have a specific need/desire to use 3.1/3.2 I'd suggest staying with 2.6 or 2.7 from python.org for now. With Activestate you have to compile yourself or use their (paid?) pypm repo.* NumPy can be installed from PyPM repo for ActivePython 2.6, 2.7, 3.1 (and the upcoming 3.2) on Windows, MacOSX and Linux. It is built with Intel's MKL and fortran compiler. http://code.activestate.com/pypm/numpy/ Note that we recently moved NumPy, SciPy and matplotlib to the free repo; so you can install it free of charge. -- Sridhar Ratnakumar Python Developer ActiveState

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ANN: ViTables 2.1 released!
by Vicent Mas Feb. 21, 2011

Feb. 21, 2011

======================= Announcing ViTables 2.1 ======================= After a long period retired from development activities I'm happy to come back and present ViTables 2.1, the latest release of this viewer for PyTables/HDF5 files. As it happens with the entire PyTables family, the main strength of ViTables is its ability to manage really large datasets in a fast and comfortable manner. For example, with ViTables you can open a table with one thousand million rows in a few tenths of second, with very low memory requirements. Also important is the fact that it is designed to be a multiplatform application, i.e., it runs flawlessly in Unix (and hence, GNU/Linux), Mac OS X and Windows. The fact that it runs on top of PyTables ensures its speed and memory efficiency. In this release you will find some bug fixes and usability enhancements. New features include: - improved queries. They are faster and eat much less memory - a simple but flexible plugins framework - human friendly formatting of times and dates for PyTables native Time fields and also for time series created via scikits.timeseries package - datasets can be imported/exported from/to files with CSV format Platforms --------- At the moment, ViTables has been fully tested only on GNU/Linux and Windows platforms, but as it is made on top of Python, PyQt and PyTables, its portability should be really good and it should work just fine in other Unices. How to get it ------------- Visit: http://www.vitables.org/download and click the link 'downloads area' to find the available packages. Share your experience --------------------- I'm very interested in your feedback about ViTables. Please send your opinions, suggestions, bugs, etc. to the ViTables Users Group at http://tech.groups.yahoo.com/group/vitables-users. Thank you! Enjoy Data with ViTables, the troll of the PyTables family! :: Share what you know, learn what you don't

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OT: performance in C extension; OpenMP, or SSE ?
by Sebastian Haase Feb. 20, 2011

Feb. 20, 2011

Hi, I assume that someone here could maybe help me, and I'm hoping it's not too much off topic. I have 2 arrays of 2d point coordinates and would like to calculate all pairwise distances as fast as possible. Going from Python/Numpy to a (Swigged) C extension already gave me a 55x speedup. (.9ms vs. 50ms for arrays of length 329 and 340). I'm using gcc on Linux. Now I'm wondering if I could go even faster !? My hope that the compiler might automagically do some SSE2 optimization got disappointed. Since I have a 4 core CPU I thought OpenMP might be an option; I never used that, and after some playing around I managed to get (only) 50% slowdown(!) :-( My code in short is this: (My SWIG typemaps use obj_to_array_no_conversion() from numpy.i) -------<Ccode> ---------- void dists2d( double *a_ps, int nx1, int na, double *b_ps, int nx2, int nb, double *dist, int nx3, int ny3) throw (char*) { if(nx1 != 2) throw (char*) "a must be of shape (n,2)"; if(nx2 != 2) throw (char*) "b must be of shape (n,2)"; if(nx3 != nb || ny3 != na) throw (char*) "c must be of shape (na,nb)"; double *dist_; int i, j; #pragma omp parallel private(dist_, j, i) { #pragma omp for nowait for(i=0;i<na;i++) { //num_threads=omp_get_num_threads(); --> 4 dist_ = dist+i*nb; // dists_ is only introduced for OpenMP for(j=0;j<nb;j++) { *dist_++ = sqrt( sq(a_ps[i*nx1] - b_ps[j*nx2]) + sq(a_ps[i*nx1+1] - b_ps[j*nx2+1]) ); } } } } -------</Ccode> ---------- There is probably a simple mistake in this code - as I said I never used OpenMP before. It should be not too difficult to use OpenMP correctly here or - maybe better - is there a simple SSE(2,3,4) version that might be even better than OpenMP... !? I supposed, that I did not get the #pragma omp lines right - any idea ? Or is it in general not possible to speed this kind of code up using OpenMP !? Thanks, Sebastian Haase

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How to tell if I succeeded to build numpy with amd, umfpack and lapack
by Samuel John Feb. 19, 2011

Feb. 19, 2011

Hi there! I have successfully built numpy 1.5 on ubuntu lucid (32 for now). I think I got ATLAS/lapack/BLAS support, and if I > ldd linalg/lapack_lite.so I see that my libptf77blas.so etc. are successfully linked. :-) However, how to I find out, if (and where) libamd.a and libumfpack.a have been found and (statically) linked. As far as I understand, I they are not present, a fallback in pure python is used, right? Is there a recommended way, I can query against which libs numpy has been built? So I can be sure numpy uses my own compiled versions of libamd, lapack and so forth. And the fftw3 is no longer supported, I guess (even if it is still mentioned in the site.cfg.example) Bests, Samuel -- Dipl.-Inform. Samuel John - - - - - - - - - - - - - - - - - - - - - - - - - PhD student, CoR-Lab(.de) and Neuroinformatics Group, Faculty of Technology, D33594 Bielefeld in cooperation with the HONDA Research Institute Europe GmbH jabber: samueljohn(a)jabber.org - - - - - - - - - - - - - - - - - - - - - - - - -

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help with translating some matlab
by Neal Becker Feb. 19, 2011

Feb. 19, 2011

I got the following matlab code from a colleage: initialization: h =zeros(1, N); %% initial filter coefficients lambda =1; delta =0.001; P =eye(N)/delta; loop: z =P*(x1'); g =z/(lambda+ x1*z); y = h*x1'; %% filter output e = ph_cpm_out(n) - y; %% error h = h + e*g'; %% adaptation Pnext=(P-g*z')/lambda; P =Pnext; So it looks to me: z is a vector The step g=z/(lambda+x1*z) seems to be a vector division. How do I translate this to numpy?

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numpy.fromiter hiding exceptions
by Åsmund Hjulstad Feb. 18, 2011

Feb. 18, 2011

Hi all, I am finding it hard to debug cases where I am creating numpy arrays from generators, and the generator function throws an exception. It seems that numpy just swallows the exception, and what I get is a not too helpful ValueError: iterator too short Much more helpfull would be to see the original exception, and get the stacktrace in ipython (or wherever I am working). Is this possible in some easy way, or am I stuck with the equivalent of if debug: mygenerator = list(mygenerator) a = np.fromiter(iter(mygenerator), dtype=xxx, count=xxx) -- Åsmund Hjulstad, asmund(a)hjulstad.com

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numpy docs dependency problem in Ubuntu
by Barry Warsaw Feb. 18, 2011

Feb. 18, 2011

I hope this message is on-topic for this mailing list! I'm working on the packaging for python-numpy 1.5 in the next version of Ubuntu (11.04 - Natty), and I'm hitting an issue that I'm hoping you can help me with. For reference, the two relevant bugs are: https://bugs.launchpad.net/ubuntu/+source/python-numpy/+bug/664276 https://bugs.launchpad.net/ubuntu/+source/python-numpy/+bug/664397 Here's the problem: for Ubuntu, we've had to disable the building of the numpy documentation package, because its dependencies violate Ubuntu policy. Numpy is in our "main" archive but the documentation depends on python-matplotlib, which lives in our "universe" archive. Such cross archive dependencies break the build. We can't put python-matplotlib in main because of *its* dependencies. You get the idea: its entire dependency stack would have to be moved to main and that's not feasible. There are a number of workarounds that we might employ to re-enable the docs, but here's what I've done so far: In conf.py: * Wrapped the 'import matplotlib' in a try-except and just issue a warning if the import fails. * use_plots = False * Comment out 'plot_directive' in extensions In doc/sphinxext/tests/test_docscrape.py: * in test_plot_examples(): * cfg = dict(use_plots=False) This lets me get through the build of the docs without the dependency on matplotlib, and a cursory look at the documentation looks pretty good. I'm inclined to propose this as a permanent workaround, but I wanted to get your feedback first. Is there a better way to build the docs without matplotlib? What are the practical effects of my workaround? Do you think this will be an acceptable solution for Ubuntu users of numpy? Cheers, -Barry

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why is www.numpy.org an HTML frame for numpy.scipy.org ??
by Sebastian Haase Feb. 17, 2011

Feb. 17, 2011

Hi, I just noticed, that www.numpy.org is now a really nice looking page -- congratulations ! But, when I click on any link - e.g. "Older python array packages" my browser shows me a new page, but keeps pointing to www.numpy.org. Obviously, www.numpy.org shows just a frame, and the real page is at http://numpy.scipy.org. I would vote, removing that frame and redirect www.numpy.org to numpy.scipy.org Is there any other use of numpy.org which I'm overlooking ? Cheers, -- Sebastian Haase '

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