Mailman 3 December 2007 - NumPy-Discussion

Epsilon for each dtype
by Matthieu Brucher 10 Dec '07

10 Dec '07

Hi, Is there somewhere a equivalent to std::numerical_limits<>::epsilon, that is, the greatest value such that 1. + epsilon is numerically equal to 1. ? I saw something that could be related in oldnumeric, but nothing in numpy itself. Matthieu -- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher

2 2

Re: [Numpy-discussion] data transit
by Nadav Horesh 08 Dec '07

08 Dec '07

Did you look at pytables (http://www.pytables.org/moin)? Nadav. -----Original Message----- From: numpy-discussion-bounces(a)scipy.org on behalf of Renato Serodio Sent: Fri 07-Dec-07 17:44 To: Discussion of Numerical Python Subject: [Numpy-discussion] data transit Hello all, I'm developing a custom computational application which I chose to base in Numpy. Already quite in love with Python, and with proprietary things making me increasingly sick (through forced exposure to stupid errors I can't correct), Numpy was the way to go. Now, it is in times like this that I regret not going for graduate studies in computation - I'm a bit locked in the old paradigms that my [fortran] generation learn. Since my application is only vaguely required to be 'generic', I had to dive into the wonderful world of computer science - a previous post in this group led to some very interesting solutions for the application, which, while doing nothing, is capable of doing everything :) A bit of context: the application is supposed to process telemetry, outputing some chart, alarm, etc. Raw data is obtained through plugin-like objects, which provide a uniform interface to distinct sources. The processing routines are objects as well, but operate on data as if they were functions (sort of sin(x)). This way, I don't need to define anything other than the interfaces - the core remains flexible. I came to a problem, though, while trying to define some structure for data transit. At first I imagined I could keep both raw data and results inside the same object; unfortunately, if I want to use these results in a second stage, my flexibility is rather impaired. Then I thought about getting raw data into an object, passing that to the processing core, and finally storing its output in another object. While this has the advantage of clearing raw data out of memory as soon as I finish chewing it, I seem to lose the relation between raw and result data sets - which I have to maintain somewhere else. Yet another issue crops up, in relation to very large data sets. If there's not enough memory to cope with the data set, one either relies on swapping or changes the algorithm - and in this case having 'inteligent' data objects allows good, textbook encapsulation. My question is thus, does anyone have experience or could point to literature/code where related problems are addressed? I understand that my application may be suffering from excessive 'generality', but certainly this problem has surfaced elsewhere. Looking forward to your answers, Renato _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion

1 0

fft inplace?
by Rolf Wester 07 Dec '07

07 Dec '07

Hi, I have huge complex arrays that have to be Fourier transformed. Is it possible to to that inplace with numpy. As far as I can remember this was possible with either Numeric or older versions of numpy, but I couldn't find any hint concerning this in the numpy manual or in the python sources. Regards Rolf

1 0

scipy.scons branch: building numpy and scipy with scons
by David Cournapeau 07 Dec '07

07 Dec '07

Hi, I've just reached a first usable scipy.scons branch, so that scipy can be built entirely with scons (assuming you build numpy with scons too). You can get it from http://svn.scipy.org/svn/scipy/branches/scipy.scons. To build it, you just need to use numpy.scons branch instead of the trunk, and use setupscons.py instead of setup.py. Again, I would be happy to hear about failures, success (please report a ticket in this case), etc... Some of the most interesting things I can think of which work with scons: - you can control fortran and C flags from the command line: CFLAGS and FFLAGS won't override necessary flags, only optimization flags, so you can easily play with warning, optimization flags. For example: CFLAGS='-W -Wall -Wextra -DDEBUG' FFLAGS='-DDEBUG -W -Wall -Wextra' python setupscons build for debugging will work. No need to care about -fPIC and co, all this is handled automatically. - dependencies are handled correctly thanks to scons: for example, if you change a library (e.g. by using MKL=None to disable mkl), only link step will be redone. platforms known to work ----------------------- - linux with gcc/g77 or gcc/gfortran (both atlas and mkl 9 were tested). - linux with intel compilers (intel and gnu compilers can also be mixed, AFAIK). - solaris with sun compilers with sunperf, only tested on indiana. Notable non working things: --------------------------- - using netlib BLAS and LAPACK is not supported (only optimized ones are available: sunperf, atlas, mkl, and vecLib/Accelerate). - parallel build does NOT work (AFAICS, this is because f2py which do some things which are not thread-safe, but I have not yet found the exact problem). - I have not yet implemented umfpack checker, and as such umfpack cannot be built yet - I have not yet tweaked fortran compiler configurations for optimizations except for gnu compilers - c++ compilers configurations are not handled either. cheers, David

6 23

Max on Complex Array
by Tom Johnson 06 Dec '07

06 Dec '07

What is the meaning of numpy's maximum on a complex-valued array?

2 2

multinomial question
by Alan G Isaac 06 Dec '07

06 Dec '07

I would think that multinomial(1,prob,size=ntrials).sum(axis=0) would be equivalent to multinomial(ntrials,prob) but the first gives a surprising result. (See below.) Explanation? Thank you, Alan Isaac >>> ntrials = 100000 >>> prob = N.arange(100,dtype=N.float32)/4950 >>> multinomial(1,prob,size=ntrials).sum(axis=0) array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 990, 1058, 996, 1102, 1088, 1137, 1144, 1150, 1196, 1198, 1272, 1273, 1268, 1265, 1380, 1336, 1371, 1405, 1348, 1420, 1515, 1506, 1571, 1499, 1556, 1517, 1603, 1691, 1696, 1763, 1622, 1716, 1722, 1785, 1866, 1799, 1918, 1818, 1868, 1938, 2010, 1916, 1950, 1983, 2062, 2079, 2224, 2165, 2136, 2156, 2215, 2118, 2309, 2389, 2377, 2423, 2328, 2325, 2469]) >>> multinomial(ntrials,prob) array([ 0, 27, 33, 55, 104, 104, 116, 153, 166, 181, 189, 199, 244, 262, 259, 303, 330, 343, 373, 360, 371, 437, 423, 470, 460, 550, 551, 497, 517, 593, 623, 623, 648, 660, 638, 718, 713, 754, 784, 831, 804, 868, 902, 851, 918, 932, 945, 972, 966, 1025, 1005, 1038, 1075, 1046, 1121, 1069, 1121, 1152, 1209, 1148, 1196, 1261, 1288, 1304, 1250, 1324, 1348, 1430, 1370, 1419, 1388, 1364, 1473, 1414, 1511, 1523, 1583, 1574, 1575, 1575, 1613, 1559, 1665, 1666, 1712, 1728, 1715, 1709, 1846, 1774, 1819, 1869, 1886, 1963, 1837, 1906, 1983, 1867, 1968, 1916]) >>>

4 5

site down...
by Fernando Perez 05 Dec '07

05 Dec '07

The whole scipy.org site seems down. Is it just on my end? Cheers, f

3 3

A quick f2py question
by Fernando Perez 05 Dec '07

05 Dec '07

Hi all, I have a quick question on f2py. I have a fortran lib I've wrapped for a while and was updating one routine today, when I noticed a message I'm curious about. The .pyf file contains this signature: subroutine createblocks(nnod,ll,nscale,nterms,pp,qq,aoffset,iflag,rintphi,rnorm) ! in :mwrep:createblocks.f integer intent(in) :: nnod integer intent(in) :: ll integer intent(in) :: nscale integer intent(hide),depend(pp) :: nterms = len(pp) real*8 dimension(nterms),intent(in) :: pp real*8 dimension(nterms),intent(in) :: qq real*8 dimension(nterms),intent(in) :: aoffset integer intent(in) :: iflag real*8 dimension(nterms*nnod*nnod),intent(out),depend(nterms,nnod) :: rintphi real*8 dimension(nterms),intent(out),depend(nterms):: rnorm end subroutine createblocks And I see this message in the build: In: mwrep.pyf:mwrep:unknown_interface:createblocks _get_depend_dict: no dependence info for 'len' The build does actually continue, and in the end, this routine seems to be correctly wrapped. But I still worry that it may be getting the right allocations by chance/accident. Is this a real error message, or just an internal warning from an intermediate pass? Or has the call for len() changed recently in f2py? (this code was originally wrapped years ago, now I'm just doing minor updates). Thanks for any info... Cheers, f

2 2

Unexpected conversion from matrix to array
by Keith Goodman 05 Dec '07

05 Dec '07

>> import numpy.matlib as M >> x = M.asmatrix(['a', 'b', 'c']) >> x == 'a' array([[ True, False, False]], dtype=bool) # <---- I expected a matrix >> x = M.asmatrix([1, 2, 3]) >> x == 1 matrix([[ True, False, False]], dtype=bool) # <---- This looks good >> >> M.__version__ '1.0.5.dev4445'

1 0

Missing file in SVN repository (test_ufunc)
by David Cournapeau 04 Dec '07

04 Dec '07

Hi, Since revision 4528, it is not possible to run the tests for numpy.core, because of a missing file, test_ufunc.py. Since the change in the test was done in r4528 whose log refers " increase of code coverage", I guess the file was not added to the repository, cheers, David

2 2