Mailman 3 January 2006 - SciPy-User

a virtual work party: help migrate scipy.org to new wiki
by Andrew Straw Jan. 28, 2006

Jan. 28, 2006

As many of you know, a number of us have been busy migrating the scipy.org website to a new, more user-friendly wiki system. The new wiki is up-and-running, is more-up-to-date, and prettier-to-look at than the old site. We are working to make scipy.org a portal website for all scientific software written in Python, and not just for the packages numpy and scipy, which happen to have their natural homes there. This wiki structure allows peer-review-by-wiki, so please do not feel bashful about updating any aspect of the wiki to suit your opinions. The current address of the new site is http://new.scipy.org/Wiki Once we go "live", we'll point http://scipy.org to this wiki. Before we make the switch, however, there are a few things that need to be done and a few things that should be done. This is a request for two things: 1) Review the page http://new.scipy.org/Wiki/MigratingFromPlone and make sure you can live with what it says. If you can't edit it. 2) Please undertake any of the tasks listed at that page. Update the page that you're working on the task, and again when you're done. 3) (Optional) Bask in the glory of our shiny new website which will allow NumPy/ SciPy/ Matplotlib/ IPython/ whateverelse to rule the scientific computing world! I'd like to ask that we try and complete these tasks as quickly as possible. I know I'm asking a busy group of people to pitch in, but I hope you'll agree the results will be worth it. Thank you, Andrew Straw PS Sorry for the cross postings. I thought it would be important to get as many hands involved as possible.

1 0

Confusing BLAS/LAPACK situation on Mac OS X
by Brian Granger Jan. 28, 2006

Jan. 28, 2006

Hello all, Thanks so much for all the work that has been going on with scipy/numpy. I have some questions/confusions about BLAS/LAPACK on Mac OS X. Everyting seems to build find. Apple's Accelerate framwork is picked up by numpy/scipy. But when I do a scipy.test I see things like: **************************************************************** WARNING: cblas module is empty ----------- See scipy/INSTALL.txt for troubleshooting. Notes: * If atlas library is not found by numpy/distutils/system_info.py, then scipy uses fblas instead of cblas. **************************************************************** and **************************************************************** WARNING: clapack module is empty ----------- See scipy/INSTALL.txt for troubleshooting. Notes: * If atlas library is not found by numpy/distutils/system_info.py, then scipy uses flapack instead of clapack. **************************************************************** Thus, It looks like the Apple supplied cblas and clapack libraries are never used, even though they are found at build time. What is the current status of using Apple's cblas and clapack? Can I use them from within scipy/numpy? How? Thanks Brian Brian Granger, Ph.D. Assistant Professor of Physics Santa Clara University bgranger(a)scu.edu Phone: 408-551-1891 Fax: 408-554-6965 This message scanned for viruses and SPAM at SCU (MGW2)

2 1

percentile
by David Abreu Rodriguez Jan. 27, 2006

Jan. 27, 2006

I do not understand this result: >>> scipy.stats.scoreatpercentile([1,2,3,4],50) 2.0750000000000002 >>> scipy.stats.percentileofscore([1,2,3,4],2.5) 50.0 I suppose that percentileofscore is the inverse function of scoreatpercentile. what is wrong? thanks

1 0

Monte Carlo package
by Ed Schofield Jan. 26, 2006

Jan. 26, 2006

Hi all, The Monte Carlo package now builds and runs fine for me on MinGW. I'd now like to ask for some help with testing on other platforms. (Are there any that don't define the rand() function? ;) If there are no cries of anguish I'll move it to the main tree on Friday. -- Ed

4 9

building scipy
by Nicholas Kottenstette Jan. 25, 2006

Jan. 25, 2006

Hey guys, I'm still a bit confused as to whether I need to install Numeric or not. Following the building scipy routines for unix http://www.scipy.org/documentation/buildscipy.txt I went and built scipy which did not generate any of the linear algebra routines. ie. unable to import LinearAlgebra So looking at the Downloads documentation, I saw that Numeric should be installed. Looking at the old Numeric documentation this would definetly give me the linear algebra interface, which I did and it works. Is this a correct conclusion: 1. In order to use scipy and utilize linear algebra features such as the ability to calculate a matrix inverse and perform a matrixmultiply I need to: a) first build and install Numeric, I used version 24.2 from http://sourceforge.net/project/showfiles.php?group_id=1369&package_id=1351 b) then build and install scipy as described in http://www.scipy.org/documentation/buildscipy.txt Looking at Travis's documentation on NumPy p. 14 and p. 15 leaves me even further confused. This tends to imply that I should build and install numpy instead of Numeric in order to get the linalg package. But is NumPy already part of scipy? Sincerely, Nicholas Kottenstette

2 2

fftpack test failures
by Paul Ray Jan. 25, 2006

Jan. 25, 2006

Hi, Since I just did an svn update for numpy and scipy to fix the stats bug, so I did a scipy.test(10), and found there are now test failures in fftpack: In [12]: scipy.fftpack.test(10) Found 24 tests for scipy.fftpack.pseudo_diffs Found 23 tests for scipy.fftpack.basic Found 4 tests for scipy.fftpack.helper Found 0 tests for __main__ Differentiation of periodic functions ===================================== size | convolve | naive ------------------------------------- 100 | 0.08 | 1.61 (secs for 1500 calls) 1000 | 0.07 | 1.94 (secs for 300 calls) 256 | 0.11 | 2.50 (secs for 1500 calls) 512 | 0.13 | 2.71 (secs for 1000 calls) 1024 | 0.09 | 3.10 (secs for 500 calls) 2048 | 0.11 | 2.57 (secs for 200 calls) 4096 | 0.12 | 2.68 (secs for 100 calls) 8192 | 0.20 | 2.82.......... (secs for 50 calls) Hilbert transform of periodic functions ========================================= size | optimized | naive ----------------------------------------- 100 | 0.08 | 1.37 (secs for 1500 calls) 1000 | 0.07 | 1.19 (secs for 300 calls) 256 | 0.10 | 1.66 (secs for 1500 calls) 512 | 0.11 | 1.70 (secs for 1000 calls) 1024 | 0.09 | 1.86 (secs for 500 calls) 2048 | 0.11 | 1.47 (secs for 200 calls) 4096 | 0.11 | 1.38 (secs for 100 calls) 8192 | 0.12 | 1.38........ (secs for 50 calls) Shifting periodic functions ============================== size | optimized | naive ------------------------------ 100 | 0.08 | 1.35 (secs for 1500 calls) 1000 | 0.06 | 1.59 (secs for 300 calls) 256 | 0.11 | 2.03 (secs for 1500 calls) 512 | 0.11 | 2.14 (secs for 1000 calls) 1024 | 0.10 | 2.57 (secs for 500 calls) 2048 | 0.10 | 1.87 (secs for 200 calls) 4096 | 0.10 | 1.76 (secs for 100 calls) 8192 | 0.13 | 1.77.. (secs for 50 calls) Tilbert transform of periodic functions ========================================= size | optimized | naive ----------------------------------------- 100 | 0.08 | 1.52 (secs for 1500 calls) 1000 | 0.05 | 1.42 (secs for 300 calls) 256 | 0.11 | 2.18 (secs for 1500 calls) 512 | 0.11 | 2.21 (secs for 1000 calls) 1024 | 0.09 | 3.00 (secs for 500 calls) 2048 | 0.20 | 2.78 (secs for 200 calls) 4096 | 0.15 | 2.57 (secs for 100 calls) 8192 | 0.12 | 1.72.... (secs for 50 calls) Fast Fourier Transform ================================================= | real input | complex input ------------------------------------------------- size | scipy | Numeric | scipy | Numeric ------------------------------------------------- 100 | 0.28 | 0.31 | 2.43 | 0.32 (secs for 7000 calls) 1000 | 0.50 | 0.76 | 3.32 | 0.82 (secs for 2000 calls) 256 | 0.68 | 0.65 | 4.76 | 0.63 (secs for 10000 calls) 512 | 0.92 | 1.35 | 8.03 | 1.27 (secs for 10000 calls) 1024 | 0.30 | 0.40 | 1.38 | 0.41 (secs for 1000 calls) 2048 | 0.62 | 0.82 | 2.56 | 0.76 (secs for 1000 calls) 4096 | 0.56 | 0.75 | 2.43 | 0.77 (secs for 500 calls) 8192 | 1.25 | 2.03 | 11.35 | 4.08...confused: size=2, arr_size=8, rank=1, effrank=2, arr.nd=2, dims=[ 2 ], arr.dims=[ 2 4 ] E (secs for 500 calls) Multi-dimensional Fast Fourier Transform =================================================== | real input | complex input --------------------------------------------------- size | scipy | Numeric | scipy | Numeric --------------------------------------------------- 100x100confused: size=100, arr_size=10000, rank=1, effrank=2, arr.nd=2, dims=[ 100 ], arr.dims=[ 100 100 ] Econfused: size=3, arr_size=27, rank=1, effrank=3, arr.nd=3, dims= [ 3 ], arr.dims=[ 3 3 3 ] Econfused: size=3, arr_size=9, rank=1, effrank=2, arr.nd=2, dims= [ 3 ], arr.dims=[ 3 3 ] Econfused: size=4, arr_size=16, rank=1, effrank=2, arr.nd=2, dims= [ 4 ], arr.dims=[ 4 4 ] Econfused: size=4, arr_size=16, rank=1, effrank=2, arr.nd=2, dims= [ 4 ], arr.dims=[ 4 4 ] E Inverse Fast Fourier Transform =============================================== | real input | complex input ----------------------------------------------- size | scipy | Numeric | scipy | Numeric ----------------------------------------------- 100 | 0.70 | 1.46 | 4.39 | 1.09 (secs for 7000 calls) 1000 | 0.57 | 2.31 | 3.40 | 2.36 (secs for 2000 calls) 256 | 0.66 | 2.27 | 5.91 | 2.58 (secs for 10000 calls) 512 | 1.09 | 4.78 | 7.63 | 4.10 (secs for 10000 calls) 1024 | 0.38 | 1.74 | 2.40 | 1.23 (secs for 1000 calls) 2048 | 0.65 | 3.74 | 3.25 | 2.11 (secs for 1000 calls) 4096 | 0.64 | 2.22 | 2.60 | 2.00 (secs for 500 calls) 8192 | 1.34 | 4.77 | 6.15 | 7.22.....confused: size=3, arr_size=9, rank=1, effrank=2, arr.nd=2, dims=[ 3 ], arr.dims=[ 3 3 ] Econfused: size=2, arr_size=4, rank=1, effrank=2, arr.nd=2, dims= [ 2 ], arr.dims=[ 2 2 ] E (secs for 500 calls) Inverse Fast Fourier Transform (real data) ================================== size | scipy | Numeric ---------------------------------- 100 | 0.57 | 1.33 (secs for 7000 calls) 1000 | 0.28 | 1.46 (secs for 2000 calls) 256 | 0.75 | 1.96 (secs for 10000 calls) 512 | 0.81 | 1.96 (secs for 10000 calls) 1024 | 0.13 | 0.51 (secs for 1000 calls) 2048 | 0.62 | 2.14 (secs for 1000 calls) 4096 | 0.71 | 1.15 (secs for 500 calls) 8192 | 1.31 | 2.52.... (secs for 500 calls) Fast Fourier Transform (real data) ================================== size | scipy | Numeric ---------------------------------- 100 | 0.40 | 0.38 (secs for 7000 calls) 1000 | 0.24 | 0.31 (secs for 2000 calls) 256 | 0.60 | 0.65 (secs for 10000 calls) 512 | 0.87 | 0.94 (secs for 10000 calls) 1024 | 0.12 | 0.14 (secs for 1000 calls) 2048 | 0.55 | 0.60 (secs for 1000 calls) 4096 | 0.51 | 0.52 (secs for 500 calls) 8192 | 0.96 | 1.09....... ====================================================================== ERROR: check_n_argument_real (scipy.fftpack.basic.test_basic.test_fft) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 105, in check_n_argument_real y = fft([x1,x2],n=4) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 101, in fft return work_function(tmp,n,1,0,overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zrfft to C/ Fortran array ====================================================================== ERROR: bench_random (scipy.fftpack.basic.test_basic.test_fftn) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 571, in bench_random y = fftn(x) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 302, in fftn return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 238, in _raw_fftnd return work_function(x,s,direction,overwrite_x=overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zfftnd to C/ Fortran array ====================================================================== ERROR: check_axes_argument (scipy.fftpack.basic.test_basic.test_fftn) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 458, in check_axes_argument assert_array_almost_equal(fftn(x),fftn(x,axes=(-3,-2,-1))) # kji_space File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 302, in fftn return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 238, in _raw_fftnd return work_function(x,s,direction,overwrite_x=overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zfftnd to C/ Fortran array ====================================================================== ERROR: check_definition (scipy.fftpack.basic.test_basic.test_fftn) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 424, in check_definition y = fftn(x) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 302, in fftn return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 238, in _raw_fftnd return work_function(x,s,direction,overwrite_x=overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zfftnd to C/ Fortran array ====================================================================== ERROR: check_shape_argument (scipy.fftpack.basic.test_basic.test_fftn) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 524, in check_shape_argument y = fftn(small_x,shape=(4,4)) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 302, in fftn return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 238, in _raw_fftnd return work_function(x,s,direction,overwrite_x=overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zfftnd to C/ Fortran array ====================================================================== ERROR: check_shape_axes_argument (scipy.fftpack.basic.test_basic.test_fftn) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 535, in check_shape_axes_argument y = fftn(small_x,shape=(4,4),axes=(-1,)) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 302, in fftn return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 253, in _raw_fftnd r = work_function(x,s,direction,overwrite_x=overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zfftnd to C/ Fortran array ====================================================================== ERROR: check_definition (scipy.fftpack.basic.test_basic.test_ifftn) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 593, in check_definition y = ifftn(x) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 331, in ifftn return _raw_fftnd(tmp,shape,axes,-1,overwrite_x,work_function) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 238, in _raw_fftnd return work_function(x,s,direction,overwrite_x=overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zfftnd to C/ Fortran array ====================================================================== ERROR: check_random_complex (scipy.fftpack.basic.test_basic.test_ifftn) ---------------------------------------------------------------------- Traceback (most recent call last): File "/sw/lib/python2.4/site-packages/scipy/fftpack/tests/ test_basic.py", line 603, in check_random_complex assert_array_almost_equal (ifftn(fftn(x)),x) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 302, in fftn return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) File "/sw/lib/python2.4/site-packages/scipy/fftpack/basic.py", line 238, in _raw_fftnd return work_function(x,s,direction,overwrite_x=overwrite_x) error: failed in converting 1st argument `x' of _fftpack.zfftnd to C/ Fortran array ---------------------------------------------------------------------- Ran 51 tests in 250.187s FAILED (errors=8) (secs for 500 calls) Out[12]: <unittest.TextTestRunner object at 0x24a3190> In [13]: scipy.__version__ Out[13]: '0.4.5.1570' In [14]: import os In [15]: os.uname() Out[15]: ('Darwin', 'mcxr2.nrl.navy.mil', '8.4.0', 'Darwin Kernel Version 8.4.0: Tue Jan 3 18:22:10 PST 2006; root:xnu-792.6.56.obj~1/RELEASE_PPC', 'Power Macintosh') In [16]: scipy.show_config() lapack_opt_info: extra_link_args = ['-Wl,-framework', '-Wl,Accelerate'] extra_compile_args = ['-faltivec'] define_macros = [('NO_ATLAS_INFO', 3)] fft_opt_info: libraries = ['fftw3'] library_dirs = ['/usr/local/lib'] define_macros = [('SCIPY_FFTW3_H', None)] include_dirs = ['/usr/local/include'] djbfft_info: NOT AVAILABLE fftw3_info: libraries = ['fftw3'] library_dirs = ['/usr/local/lib'] define_macros = [('SCIPY_FFTW3_H', None)] include_dirs = ['/usr/local/include'] blas_opt_info: extra_link_args = ['-Wl,-framework', '-Wl,Accelerate'] extra_compile_args = ['-faltivec', '-I/System/Library/Frameworks/ vecLib.framework/Headers'] define_macros = [('NO_ATLAS_INFO', 3)] Finally, if it matters, here are the FFTW2 and FFTW3 libraries on my system: mcxr2 : 160>ls /usr/local/lib/*fft* /usr/local/lib/libfftw3.a /usr/local/lib/libsfftw.a /usr/local/lib/libfftw3.la* /usr/local/lib/libsfftw.la* /usr/local/lib/libfftw3_threads.a /usr/local/lib/ libsfftw_threads.a /usr/local/lib/libfftw3_threads.la* /usr/local/lib/ libsfftw_threads.la* /usr/local/lib/libfftw3f.a /usr/local/lib/libsrfftw.a /usr/local/lib/libfftw3f.la* /usr/local/lib/libsrfftw.la* /usr/local/lib/libfftw3f_threads.a /usr/local/lib/ libsrfftw_threads.a /usr/local/lib/libfftw3f_threads.la* /usr/local/lib/ libsrfftw_threads.la* Cheers, -- Paul -- Dr. Paul S. Ray E-mail: Paul.Ray(a)nrl.navy.mil Naval Research Laboratory WWW : http://xweb.nrl.navy.mil/ personnel/paulr/ Code 7655 Phone : (202) 404-1619 Washington, DC 20375 AIM : NRLPSR

6 6

Strange behavior with scipy.test()
by Travis Brady Jan. 24, 2006

Jan. 24, 2006

I've just installed Numpy-0.9.2 and Scipy-0.4.4 from the windows binaries on another box I have access to and Numpy.test() works as expected by scipy.test() yields the following strange message and kicks me out of ipython (0.7.1): In [1]: import scipy In [2]: scipy.test() Overwriting lib=<module 'scipy.lib' from 'C:\Python24\lib\site-packages\scipy\li b\__init__.pyc'> from C:\Python24\lib\site-packages\scipy\lib\__init__.pyc (was <module 'numpy.lib' from 'C:\Python24\lib\site-packages\numpy\lib\__init__.pyc'> from C:\Python24\lib\site-packages\numpy\lib\__init__.pyc) Found 128 tests for scipy.linalg.fblas Found 10 tests for scipy.stats.morestats Found 92 tests for scipy.stats.stats Found 36 tests for scipy.linalg.decomp Found 6 tests for scipy.optimize.optimize Found 4 tests for scipy.linalg.lapack Found 1 tests for scipy.optimize.zeros Found 92 tests for scipy.stats Found 41 tests for scipy.linalg.basic Found 339 tests for scipy.special.basic Found 128 tests for scipy.lib.blas.fblas Found 7 tests for scipy.linalg.matfuncs Found 42 tests for scipy.lib.lapack Found 1 tests for scipy.optimize.cobyla Found 14 tests for scipy.lib.blas Found 14 tests for scipy.linalg.blas Found 70 tests for scipy.stats.distributions Found 6 tests for scipy.optimize Found 0 tests for __main__ EEEEEEEEEEEEE C:\Python24\Scripts>

1 0

Behavior of string.join differs from standard python
by Yann Le Du Jan. 24, 2006

Jan. 24, 2006

Hello, Just a small problem I stumbled upon. I'd like to know why the behavior of string.join in scipy is different from that of standard python ? In scipy : # string.join("",["a","b","c"]) and in python string module : # string.join(["a","vb",",cf"],"") And also, the documentation of string.join is not clear, because if you do what's written there, there's an error. -- Yann Le Du http://yledu.free.fr

3 2

Bug in stats?
by Paul Ray Jan. 24, 2006

Jan. 24, 2006

Hi, I'm getting nan when I try to calculate the stats on any distribution In [1]: import scipy Overwriting fft=<function fft at 0x1458730> from scipy.fftpack.basic (was <function fft at 0x14478b0> from numpy.dft.fftpack) Overwriting ifft=<function ifft at 0x1458770> from scipy.fftpack.basic (was <function inverse_fft at 0x14478f0> from numpy.dft.fftpack) In [2]: scipy.stats.norm.stats() Out[2]: (nan, nan) In [3]: scipy.__version__ Out[3]: '0.4.4' I get nan's for all distributions I have tried and the loc and scale parameters don't seem to help. Is this a bug or am I using it wrong? Thanks, -- Paul -- Dr. Paul S. Ray E-mail: Paul.Ray(a)nrl.navy.mil Naval Research Laboratory WWW : http://xweb.nrl.navy.mil/ personnel/paulr/ Code 7655 Phone : (202) 404-1619 Washington, DC 20375 AIM : NRLPSR

3 6

multinomial-like randint
by daishi＠egcrc.net Jan. 24, 2006

Jan. 24, 2006

hi, i'm wondering if i'm missing a function which does something along the lines of the following: --- import scipy def randomint(f): """Sample from [0, len(f)-1] according to f, which doesn't have to be normalized. """ n = len(f) s = scipy.sum(f) if n == 0 or s == 0.0: raise ValueError('Input to randomint must be nonzero') r = scipy.random.random() for i in xrange(n): x = f[i]/s if r < x: return i r -= x raise RuntimeError('Error in randomint, fell off of end') --- i've found scipy.random.randint which is uniform, and scipy.random.multinomial, which returns multi-sample counts which i don't need. i realize the above is pretty simple, but i imagine that this must be a relatively common need, and i'd rather not loop in python, so i feel like i must not be looking in the right place. tia,d

2 1