Mailman 3 October 2008 - NumPy-Discussion

problems multiplying poly1d with number from array
by Mark Bakker 27 Oct '08

27 Oct '08

Hello list - I can multiply a poly1d instance with a number, but when I multiply with a number from an array, the order matters. That seems a bug: >>> a = array([2]) >>> p = poly1d([1,2]) >>> print 2*p # Works 2 x + 4 >>> print a[0]*p # Doesn't work, returns an array [2 4] >>> print p*a[0] # Works 2 x + 4

1 0

How to do: y[y<T] = y+T
by Nicolas ROUX 27 Oct '08

27 Oct '08

Hello, I hope this is not a silly question ;-) I have a Numpy array, and I want to process it with : "if the value is lower than Threshold, then increase by Threshold" I would like to translate it as: y[y<Treshold] = y + Treshold To benefit from the Numpy speed. But this doesn't work, any idea ? Thanks, Cheers, Nicolas.

6 8

Optimization of loops
by Pierre Yger 27 Oct '08

27 Oct '08

Hi all, This is my first mail to the mailing list, and I would like to know if anybody has a great idea about the use or not of Numpy and loops in Python. So here is my problem : I've a large list of tuple (id, time), id being integer between [0, ..., N] and time float values. I want to have a mysort() function that will be able to explode this list into N lists of differents sizes, that will contained the times associated to each id. Example: >> spikes = [(0, 2.3),(1, 5.6),(3, 2.5),(0, 5.2),(3, 10.2),(2, 16.2)] mysort(spikes) should return: [[2.3, 5.2], [5.6], [16.2], [2.5, 10.2]] Intuitively, the simplest way to do that is to append elements while going through all the tuples of the main list (called spikes) to empty lists: res = [[] for i in xrange(N)] for id, time in my_list: res[id].append(time) But this loop seems to be incredibly slow for large lists ! A faster way (after having performed some profiling) seems to do: spikes = numpy.array(spikes) # Convert the list into a numpy array res = [] for id in xrange(N): res.append(spikes[spikes[:,0] == id, 1]) # Use Numpy indexes Nevertheless, this is still rather slow. Does anybody have any idea about a faster way to do this ? Is there a Numpy function that could be used ? Thanks in advance, Pierre

6 5

docs.scipy.org -- new site for the documentation marathon
by Pauli Virtanen 26 Oct '08

26 Oct '08

Hi all, Gaël and I finished moving the Scipy documentation editor web app to a new site (at Enthought, thanks!). The Numpy documentation marathon can now be found at http://docs.scipy.org/numpy/ instead of the old location. Everything should work as previously, or even better, but if something is broken, please notify me. *** The domain name was, however, very tempting for hosting also the Sphinx-generated documentation, so we put those there too. You can browse to http://docs.scipy.org/ to see how they currently look like. Suggestions how to improve this are welcome! Currently, the sources for the Sphinx stuff can be found here: https://code.launchpad.net/~pauli-virtanen/scipy/numpy-refguide http://www.iki.fi/pav/tmp/scipy-refguide.tar.gz http://www.iki.fi/pav/tmp/docsscipy-frontpage.tar.gz (The latter two are tarballs because of problems with launchpad.net and apparently Bzr 1.6.1.) But I think I will move all of them to Scipy's SVN -- I believe the advantages of using the same revision control system as numpy and scipy themselves will outweigh those of using Bzr. Eventually, we can also make all this documentation editable on the web via the Documentation marathon app. (Actually, we already could, but creating new files is not possible using the app yet, and there are some performance issues remaining in rendering long documentation files.) *** Now, the role of docs.scipy.org warrants discussion, because on the one hand, the domain "docs.scipy.org" looks very official, and on the other hand, "scipy.org/Documentation" claims to be the place for official documentation. What do you think: should we use the current front page of docs.scipy.org, shifting the focus and entry point of documentation to the Sphinx-generated pages, or still keep the focus on the Moin wiki at scipy.org? I'd like to see a move towards a full dedicated documentation site -- I believe using Sphinx is the way to go in the future, and documentation generated by it does have a reassuring and clear visual appearance. Also, making the Sphinx documentation more prominent could help people to focus documentation efforts, and write more of it :) Cheers, -- Pauli Virtanen

3 3

NumPy 1.2.1 to be tagged 10/28/08
by Jarrod Millman 26 Oct '08

26 Oct '08

Hey, I plan to release NumPy 1.2.1 before the SciPy 0.7 sprint that Stefan is organizing for Nov. 1-2. So I will be tagging the release on Wednesday, Oct. 29th. Is anyone planning to back port anymore fixes to the 1.2.x branch? If so, please do so by Tuesday, Oct. 28th. Here is what has been back ported (http://projects.scipy.org/scipy/numpy/log/branches/1.2.x): bug fix for subclassing object arrays: http://projects.scipy.org/scipy/numpy/changeset/5891 MaskedArray fixes: http://projects.scipy.org/scipy/numpy/changeset/5936 http://projects.scipy.org/scipy/numpy/changeset/5948 Python 2.4 compatible lookfor: http://projects.scipy.org/scipy/numpy/changeset/5945 Setuptools fix: http://projects.scipy.org/scipy/numpy/changeset/5956 Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/

1 0

ANN: Python programs for epidemic modelling
by I. Soumpasis 26 Oct '08

26 Oct '08

Dear lists, DeductiveThinking.com now provides the Python programs for the book of M. Keeling & P. Rohani "Modeling Infectious Diseases in Humans and Animals", Princeton University Press, 2008. The book has on-line material which includes programs for different models in various programming languages and mathematical tools such as, "C++, FORTRAN and Matlab, while some are also coded in the web-based Java programming language to allow readers to quickly experiment with these types of models", as it is stated at the website. The Python version of the programs were written long ago and submitted to the book's on line material website (available soon). The Python programs with the basic equations modelled and the results in figures were now uploaded on a special wiki page of DeductiveThinking.com. Since, the programs are heavily using numpy, scipy and matplotlib libraries, I send this announcement to all the three lists and the main python-list; sorry for double-posting. The announcement with the related links is uploaded here http://blog.deductivethinking.com/?p=29. The programs are at http://wiki.deductivethinking.com/wiki/Python_Programs_for_Modelling_Infect…. For those who are interested on modelling and epidemiology, they can take a look at the main site (http://deductivethinking.com) or the main page of the wiki (http://wiki.deductivethinking.com) and follow the epidemiology links. The website is in its beginning, so limited information is uploaded up to now. Thanks for your time and I hope it will be useful for some people, Best Regards, Ilias Soumpasis

3 4

problem installing numpy using scons
by Hoyt Koepke 25 Oct '08

25 Oct '08

Hello, I'm trying to install the latest numpy using setupscons.py, but it gives me an error that I can't track down. It's in the latest numpy release (5946). with the latest numpyscons from the 0.9.3 branch. Here's the last part of output: is bootstrapping ? True Executing scons command (pkg is numpy.core): /usr/local2/bin/python "/usr/local2/lib/python2.5/site-packages/numscons-0.9.3dev-py2.5.egg/numscons/scons-local/scons.py" -f numpy/core/SConstruct -I. scons_tool_path="" src_dir="numpy/core" pkg_name="numpy.core" log_level=50 distutils_libdir="../../../../build/lib.linux-x86_64-2.5" cc_opt=gcc cc_opt_path="/usr/bin" f77_opt=gfortran f77_opt_path="/usr/bin" cxx_opt=g++ cxx_opt_path="/usr/bin" include_bootstrap=../../../../numpy/core/include silent=0 bootstrapping=1 scons: Reading SConscript files ... Checking for C header file Python.h... yes Checking size of short ... yes Checking size of int ... yes Checking size of long ... yes Checking size of float ... yes Checking size of double ... yes Checking size of long double ... yes Checking size of Py_intptr_t ... yes Checking whether PY_LONG_LONG is declared... yes Checking size of PY_LONG_LONG ... yes Checking whether CHAR_BIT is declared... yes Checking whether PRIdPTR is declared... yes Checking if math lib [] is usable for numpy ... No ! Checking for C library m... yes Checking if math lib ['m'] is usable for numpy ... Yes ! AttributeError: SConfBase instance has no attribute 'CheckFuncsAtOnce': File "/usr/local2/src/numpy/numpy/core/SConstruct", line 2: GetInitEnvironment(ARGUMENTS).DistutilsSConscript('SConscript') File "/usr/local2/lib/python2.5/site-packages/numscons-0.9.3dev-py2.5.egg/numscons/core/numpyenv.py", line 108: build_dir = '$build_dir', src_dir = '$src_dir') File "/usr/local2/lib/python2.5/site-packages/numscons-0.9.3dev-py2.5.egg/numscons/scons-local/scons-local-1.0.1/SCons/Script/SConscript.py", line 533: return apply(_SConscript, [self.fs,] + files, subst_kw) File "/usr/local2/lib/python2.5/site-packages/numscons-0.9.3dev-py2.5.egg/numscons/scons-local/scons-local-1.0.1/SCons/Script/SConscript.py", line 256: exec _file_ in call_stack[-1].globals File "/usr/local2/src/numpy/build/scons/numpy/core/SConscript", line 165: check_funcs(optional_stdfuncs) File "/usr/local2/src/numpy/build/scons/numpy/core/SConscript", line 154: st = config.CheckFuncsAtOnce(funcs) error: Error while executing scons command. See above for more information. If you think it is a problem in numscons, you can also try executing the scons command with --log-level option for more detailed output of what numscons is doing, for example --log-level=0; the lowest the level is, the more detailed the output it. I'm trying to install it on a 8 processor 64 bit Intel machine. I could post more details of the installation if needed, but they don't seem relevant here (except that I've had a lot of issues getting the regular install script to work with the correct compile parameters, and so I thought I'd try scons). If there's something I'm doing wrong, let me know; otherwise I'd be happy to post a bug report. --Hoyt

2 3

help vectorizing something
by Mathew Yeates 24 Oct '08

24 Oct '08

Hi I have 2 vectors A and B. For each value in A I want to find the location in B of the same value. Both A and B have unique elements. Of course I could something like For each index of A: v =A[index] location = numpy.where(B == v) But I have very large lists and it will take too long. Thanks to any one of you vectorization gurus that has any ideas. Mathew

2 3

creating a Numeric array from a numpy array LEAKS memory
by Jose Borreguero 24 Oct '08

24 Oct '08

Dear numpy users, I need to pass a Numeric array to some oldie code from a numpy array. I decided to go like this: for i in range(BIGNUMER): my_numpy_array=grabArray(i) na=Numeric.array( my_numpy_array, Numeric.Float) oldie_code(na) The constructor line: na=Numeric.array( my_numpy_array, Numeric.Float) does leak memory. Is there a way to pass the Numeric array to oldie_code without the leaks? Regards, -- Jose M. Borreguero Postdoctoral Associate Oak Ridge National Laboratory P.O. Box 2008, M.S. 6164 Oak Ridge, TN 37831 phone: 865-241-3071 fax: 865-576-5491 Email: borreguerojm(a)ornl.gov

4 5

Re: [Numpy-discussion] (Late) summary of PEP-225 discussion at Scipy
by Fernando Perez 22 Oct '08

22 Oct '08

Hi Please keep your replies on list. It's important that this thread in particular is publicly archived, since it may serve as the reference for further discussions. On Wed, Oct 22, 2008 at 3:37 PM, Sebastien Binet <binet(a)cern.ch> wrote: > Fernando, > >> much delayed, but here it is, finally. The doc regarding our >> discussion about PEP 225 is attached, and I'm keeping a public copy >> for viewing convenience (with html) here: >> >> https://cirl.berkeley.edu/fperez/static/numpy-pep225/ >> >> Note that, as indicated in the link above, the real doc is in bzr, so >> you can check it out to generate patches or a branch (the preferred >> formats for big changes). >> >> This is just a first cut, going from memory and notes. I'd appreciate >> any feedback, corrections, etc. > > what about the D-language-like syntax I proposed which has the advantage of not > introducing gazillions of new operators (but just stating that the operation > will be broadcasted/vectorized) ? > > # create array of doubles > a = ... > # add 42 to all elements > a[] += 42. > # apply a function > b = sin(a[]) > # or this? > b = sin[](a) > > c = a[] + b[] Regardless of its merits in D, I'd bet this has little chance of flying in python. The pep is simply about adding some special methods with matching operator form, inline with existing python syntactic tradition. This suggestion is a pretty radical departure from Python's existing style. Having said that, I'm not Guido and I could be 100% wrong here, so I'll most certainly put your comments in when I update the document later. >> I'm giving a talk at the BayPiggies >> group Nov 13 about SciPy > count me in ;) > > cheers, > sebastien. > -- > ################################### > # Dr. Sebastien Binet > # Lawrence Berkeley National Lab. > # 1 Cyclotron Road > # Berkeley, CA 94720 > ################################### > > Cheers, f

1 0