Mailman 3 May 2009 - NumPy-Discussion

skiprows option in loadtxt
by Nils Wagner 20 May '09

20 May '09

Hi all, Is the value of skiprows in loadtxt restricted to values in [0-10] ? It doesn't work for skiprows=11. Nils

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numpy failure under Windows Vista 64 bit
by "Klaus Nökel" 20 May '09

20 May '09

> > > > I don't believe that the problem is specific to IDLE. Python also > crashes when I put nothing but "import numpy" in a file and execute it with > python.exe. > > > > Regarding the note on building numpy myself: the discussion in this > forum scared me a little, because of the challenge to build LAPACK with a > compatible Fortran compiler etc. That and the fact that I do not have MSVC 2008 > (only 2005) keeps me from trying it. Any chance that a MS-based installer > will materialize soon? Or are there any mingw-specific runtime libraries > that I need to install so that the mingw-based numpy works? > > > > Thanks for your help! > > Klaus Noekel > > > > > > > > > Hi, > I also see this. > What version of Python specific of Python are you using? > I got the same with Python 2.6.1 so perhaps I need to downgrade to > Python 2.6.0? > > Bruce I got it with a fresh 2.6.2 install on Tuesday. Klaus -- Neu: GMX FreeDSL Komplettanschluss mit DSL 6.000 Flatrate + Telefonanschluss für nur 17,95 Euro/mtl.!* http://dslspecial.gmx.de/freedsl-aktionspreis/?ac=OM.AD.PD003K11308T4569a

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Re: [Numpy-discussion] Overlap arrays with "transparency"
by Cristi Constantin 20 May '09

20 May '09

Thank you for your help. :) I used this : try: NData[ (NData==transparent)[:len(OData)] ] = OData[ (NData==transparent)[:len(OData)] ] except: pass That means overwrite all "transparent" data from NData with valid data from OData. I am sure it's not the best method yet, but it's the only one that works. --- On Mon, 5/18/09, Cristi Constantin <darkgl0w(a)yahoo.com> wrote: From: Cristi Constantin <darkgl0w(a)yahoo.com> Subject: [Numpy-discussion] Overlap arrays with "transparency" To: "Numpy Discussion" <numpy-discussion(a)scipy.org> Date: Monday, May 18, 2009, 5:37 AM Good day. I am working on this algorithm for a few weeks now, so i tried almost everything... I want to overlap / overwrite 2 matrices, but completely ignore some values (in this case ignore 0) Let me explain: a = [ [1, 2, 3, 4, 5], [9,7], [0,0,0,0,0], [5,5,5] ] b = [ [0,0,9,9], [1,1,1,1], [2,2,2,2] ] Then, we have: a over b = [ [1,2,3,4,5], [9,7,1,1], [1,1,1,1,0], [5,5,5,2] ] b over a = [ [0,0,9,9,5], 1,1,1,1], 2,2,2,2,0], 5,5,5] ] That means, completely overwrite one list of arrays over the other, not matter what values one has, not matter the size, just ignore 0 values on overwriting. I checked the documentation, i just need some tips. TempA = [[]] # One For Cicle in here to get the Element data... Data = vElem.data # This is a list of numpy ndarrays. # for nr_row in range( len(Data) ): # For each numpy ndarray (row) in Data. # NData = Data[nr_row] # New data, to be written over old data. OData = TempA[nr_row:nr_row+1] or [[]] # This is old data. Can be numpy ndarray, or empty list. OData = OData[0] # # NData must completely eliminate transparent pixels... here comes the algorithm... No algorithm yet. # if len(NData) >= len(OData): # If new data is longer than old data, old data will be completely overwritten. TempA[nr_row:nr_row+1] = [NData] else: # Old data is longer than new data ; old data cannot be null. TempB = np.copy(OData) TempB.put( range(len(NData)), NData ) #TempB[0:len(NData)-1] = NData # This returns "ValueError: shape mismatch: objects cannot be broadcast to a single shape" TempA[nr_row:nr_row+1] = [TempB] del TempB # # # The result is stored inside TempA as list of numpy arrays. I would use 2D arrays, but they are slower than Python Lists containing Numpy arrays. I need to do this overwrite in a very big loop and every delay is very important. I tried to create a masked array where all "zero" values are ignored on overlap, but it doesn't work. Masked or not, the "transparent" values are still overwritten. Please, any suggestion is useful. Thank you.

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Rotate Left and Rotate Right
by Cristi Constantin 20 May '09

20 May '09

Good day, me again. I have this string of data : String = 'i want\nto go\nto the\nbeach'. I want to rotate this data to left, or to right, after is split it after '\n'. Note that it's important to use 'U' array, because i might have unicode characters in this string. So normal, the text is: i want to go to the beach Rotated right would be: btti eoo a w ctga hhon e t Rotated left would be: t e nohh agtc w a ooe ittb There are a few methods i guess could be used. Split like: [np.array([j for j in i],'U') for i in String.split('\n')] => [ array([u'i', u' ', u'w', u'a', u'n', u't'], dtype='<U1'), array([u't', u'o', u' ', u'g', u'o'], dtype='<U1'), array([u't', u'o', u' ', u't', u'h', u'e'], dtype='<U1'), array([u'b', u'e', u'a', u'c', u'h'], dtype='<U1') ] Or split like: np.array( String.split('\n'), 'U' ), => array([u'i want', u'to go', u'to the', u'beach'], dtype='<U6') Both methods are impossible to use with my knowledge. Without Numpy, if i want to rotate to right: i should reverse a list of splited by '\n' strings, save max length for all lines, align all lines to max len, rotate the square with map( lambda *row: [elem for elem in row], *Content ), then join the resulted sub-lists, unite with '\n' and return. I need to know if there is a faster Numpy approach... maybe explode the string by '\n' and rotate fast, or something? Thank you very much, in advance.

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build failure with macosx-10.5-fat64-2.6
by David Warde-Farley 20 May '09

20 May '09

I've just tried a "fat 64" build (with a Python 2.6.2 that had been built similarly), and I'm getting this weird behaviour. The command I used was: CFLAGS="-O3 -Wall -DNDEBUG -g -fwrapv -Wstrict-prototypes -arch x86_64 -arch ppc64" python setup.py build It looks as though for some reason, numpy distutils is executing gcc without any -arch flags, and falling back to the guessed architecture ppc (which in my cases is not even partially correct, as I was building x86_64 and ppc64 only). Building with CC="gcc -arch x86_64 -arch ppc64" fixes things, but I guess this is a bug in numpy distutils if it's not respecting CFLAGS during these config tests. Output is below. Cheers, David ------------ C compiler: gcc -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -O3 - Wall -DNDEBUG -g -fwrapv -Wstrict-prototypes -arch x86_64 -arch ppc64 compile options: '-Inumpy/core/src -Inumpy/core/src/multiarray -Inumpy/ core/src/umath -Inumpy/core/include -I/Library/Frameworks/ Python64.framework/Versions/2.6/include/python2.6 -c' gcc: _configtest.c _configtest.c:1: warning: conflicting types for built-in function ‘exp’ _configtest.c:1: warning: conflicting types for built-in function ‘exp’ gcc _configtest.o -o _configtest ld warning: in _configtest.o, missing required architecture ppc in file Undefined symbols: "_main", referenced from: start in crt1.10.5.o ld: symbol(s) not found collect2: ld returned 1 exit status ld warning: in _configtest.o, missing required architecture ppc in file Undefined symbols: "_main", referenced from: start in crt1.10.5.o ld: symbol(s) not found collect2: ld returned 1 exit status failure. removing: _configtest.c _configtest.o C compiler: gcc -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -O3 - Wall -DNDEBUG -g -fwrapv -Wstrict-prototypes -arch x86_64 -arch ppc64 compile options: '-Inumpy/core/src -Inumpy/core/src/multiarray -Inumpy/ core/src/umath -Inumpy/core/include -I/Library/Frameworks/ Python64.framework/Versions/2.6/include/python2.6 -c' gcc: _configtest.c _configtest.c:1: warning: conflicting types for built-in function ‘exp’ _configtest.c:1: warning: conflicting types for built-in function ‘exp’ gcc _configtest.o -lm -o _configtest ld warning: in _configtest.o, missing required architecture ppc in file Undefined symbols: "_main", referenced from: start in crt1.10.5.o ld: symbol(s) not found collect2: ld returned 1 exit status ld warning: in _configtest.o, missing required architecture ppc in file Undefined symbols: "_main", referenced from: start in crt1.10.5.o ld: symbol(s) not found collect2: ld returned 1 exit status failure. removing: _configtest.c _configtest.o C compiler: gcc -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -O3 - Wall -DNDEBUG -g -fwrapv -Wstrict-prototypes -arch x86_64 -arch ppc64 compile options: '-Inumpy/core/src -Inumpy/core/src/multiarray -Inumpy/ core/src/umath -Inumpy/core/include -I/Library/Frameworks/ Python64.framework/Versions/2.6/include/python2.6 -c' gcc: _configtest.c _configtest.c:1: warning: conflicting types for built-in function ‘exp’ _configtest.c:1: warning: conflicting types for built-in function ‘exp’ gcc _configtest.o -lcpml -o _configtest ld: library not found for -lcpml collect2: ld returned 1 exit status ld: library not found for -lcpml collect2: ld returned 1 exit status failure. removing: _configtest.c _configtest.o Traceback (most recent call last): File "setup.py", line 172, in <module> setup_package() File "setup.py", line 165, in setup_package configuration=configuration ) File "/Users/dwf/src/numpy-svn/numpy/distutils/core.py", line 184, in setup return old_setup(**new_attr) File "/Library/Frameworks/Python64.framework/Versions/2.6/lib/ python2.6/distutils/core.py", line 152, in setup dist.run_commands() File "/Library/Frameworks/Python64.framework/Versions/2.6/lib/ python2.6/distutils/dist.py", line 975, in run_commands self.run_command(cmd) File "/Library/Frameworks/Python64.framework/Versions/2.6/lib/ python2.6/distutils/dist.py", line 995, in run_command cmd_obj.run() File "/Users/dwf/src/numpy-svn/numpy/distutils/command/build.py", line 37, in run old_build.run(self) File "/Library/Frameworks/Python64.framework/Versions/2.6/lib/ python2.6/distutils/command/build.py", line 134, in run self.run_command(cmd_name) File "/Library/Frameworks/Python64.framework/Versions/2.6/lib/ python2.6/distutils/cmd.py", line 333, in run_command self.distribution.run_command(command) File "/Library/Frameworks/Python64.framework/Versions/2.6/lib/ python2.6/distutils/dist.py", line 995, in run_command cmd_obj.run() File "/Users/dwf/src/numpy-svn/numpy/distutils/command/ build_src.py", line 130, in run self.build_sources() File "/Users/dwf/src/numpy-svn/numpy/distutils/command/ build_src.py", line 147, in build_sources self.build_extension_sources(ext) File "/Users/dwf/src/numpy-svn/numpy/distutils/command/ build_src.py", line 250, in build_extension_sources sources = self.generate_sources(sources, ext) File "/Users/dwf/src/numpy-svn/numpy/distutils/command/ build_src.py", line 307, in generate_sources source = func(extension, build_dir) File "numpy/core/setup.py", line 337, in generate_config_h mathlibs = check_mathlib(config_cmd) File "numpy/core/setup.py", line 281, in check_mathlib raise EnvironmentError("math library missing; rerun " EnvironmentError: math library missing; rerun setup.py after setting the MATHLIB env variable

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numpy failure under Windows Vista 64 bit
by Klaus Noekel 19 May '09

19 May '09

Today I wanted to experiment with the AMD64 version of numpy. I am using Windows Vista 64 bit. I downloaded and installed today's Python 2.6.2 AMD64 and then numpy 1.3.0 AMD64. Executing "import numpy" (nothing else) yielded the following error message: IDLE 2.6.2 >>> import numpy Warning (from warnings module): File "C:\Python26\lib\site-packages\numpy\core\__init__.py", line 5 import multiarray Warning: Windows 64 bits support is experimental, and only available for testing. You are advised not to use it for production. CRASHES ARE TO BE EXPECTED - PLEASE REPORT THEM TO NUMPY DEVELOPERS Traceback (most recent call last): File "<pyshell#0>", line 1, in <module> import numpy File "C:\Python26\lib\site-packages\numpy\__init__.py", line 130, in <module> import add_newdocs File "C:\Python26\lib\site-packages\numpy\add_newdocs.py", line 9, in <module> from lib import add_newdoc File "C:\Python26\lib\site-packages\numpy\lib\__init__.py", line 13, in <module> from polynomial import * File "C:\Python26\lib\site-packages\numpy\lib\polynomial.py", line 18, in <module> from numpy.linalg import eigvals, lstsq File "C:\Python26\lib\site-packages\numpy\linalg\__init__.py", line 47, in <module> from linalg import * File "C:\Python26\lib\site-packages\numpy\linalg\linalg.py", line 22, in <module> from numpy.linalg import lapack_lite ImportError: DLL load failed: Das angegebene Modul wurde nicht gefunden. I doubt that the DLL was not physically present and rather suspect a dependency on some other DLL that was missing. The INSTALL.TXT unfortunately was not helpful. Can anybody please explain what other dependencies exist? Anything else I need to install? Thanks a lot! Klaus Noekel Karlsruhe, Germany

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(no subject)
by David J Strozzi 19 May '09

19 May '09

Hi, [You may want to edit the numpy homepage numpy.scipy.org to tell people they must subscribe to post, and adding a link to http://www.scipy.org/Mailing_Lists] Many of you probably know of the interpreter yorick by Dave Munro. As a Livermoron, I use it all the time. There are some built-in functions there, analogous to but above and beyond numpy's sum() and diff(), which are quite useful for common operations on gridded data. Of course one can write their own, but maybe they should be cleanly canonized? For instance: x = linspace(0,10,10) y = sin(x) It is common, say when integrating y(x), to take "point-centered" data and want to zone-center it: I = sum(zcen(y)*diff(x)) def zcen(x): return 0.5*(x[0:-1]+x[1:]) Besides zcen, yorick has builtins for "point centering", "un-zone centering," etc. Also, due to its slick syntax you can give these things as array "indexes": x(zcen), y(dif), z(:,sum,:) Just some thoughts, David Strozzi

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Overlap arrays with "transparency"
by Cristi Constantin 18 May '09

18 May '09

Good day. I am working on this algorithm for a few weeks now, so i tried almost everything... I want to overlap / overwrite 2 matrices, but completely ignore some values (in this case ignore 0) Let me explain: a = [ [1, 2, 3, 4, 5], [9,7], [0,0,0,0,0], [5,5,5] ] b = [ [0,0,9,9], [1,1,1,1], [2,2,2,2] ] Then, we have: a over b = [ [1,2,3,4,5], [9,7,1,1], [1,1,1,1,0], [5,5,5,2] ] b over a = [ [0,0,9,9,5], 1,1,1,1], 2,2,2,2,0], 5,5,5] ] That means, completely overwrite one list of arrays over the other, not matter what values one has, not matter the size, just ignore 0 values on overwriting. I checked the documentation, i just need some tips. TempA = [[]] # One For Cicle in here to get the Element data... Data = vElem.data # This is a list of numpy ndarrays. # for nr_row in range( len(Data) ): # For each numpy ndarray (row) in Data. # NData = Data[nr_row] # New data, to be written over old data. OData = TempA[nr_row:nr_row+1] or [[]] # This is old data. Can be numpy ndarray, or empty list. OData = OData[0] # # NData must completely eliminate transparent pixels... here comes the algorithm... No algorithm yet. # if len(NData) >= len(OData): # If new data is longer than old data, old data will be completely overwritten. TempA[nr_row:nr_row+1] = [NData] else: # Old data is longer than new data ; old data cannot be null. TempB = np.copy(OData) TempB.put( range(len(NData)), NData ) #TempB[0:len(NData)-1] = NData # This returns "ValueError: shape mismatch: objects cannot be broadcast to a single shape" TempA[nr_row:nr_row+1] = [TempB] del TempB # # # The result is stored inside TempA as list of numpy arrays. I would use 2D arrays, but they are slower than Python Lists containing Numpy arrays. I need to do this overwrite in a very big loop and every delay is very important. I tried to create a masked array where all "zero" values are ignored on overlap, but it doesn't work. Masked or not, the "transparent" values are still overwritten. Please, any suggestion is useful. Thank you.

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linear algebra help
by Quilby 18 May '09

18 May '09

Hi- This is what I need to do- I have this equation- Ax = y Where A is a rational m*n matrix (m<=n), and x and y are vectors of the right size. I know A and y, I don't know what x is equal to. I also know that there is no x where Ax equals exactly y. I want to find the vector x' such that Ax' is as close as possible to y. Meaning that (Ax' - y) is as close as possible to (0,0,0,...0). I know that I need to use either the lstsq function: http://www.scipy.org/doc/numpy_api_docs/numpy.linalg.linalg.html#lstsq or the svd function: http://www.scipy.org/doc/numpy_api_docs/numpy.linalg.linalg.html#svd I don't understand the documentation at all. Can someone please show me how to use these functions to solve my problem. Thanks a lot!!! -quilby

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SciPy 2009 Call for Papers
by Jarrod Millman 18 May '09

18 May '09

========================== SciPy 2009 Call for Papers ========================== SciPy 2009, the 8th Python in Science conference, will be held from August 18-23, 2009 at Caltech in Pasadena, CA, USA. Each year SciPy attracts leading figures in research and scientific software development with Python from a wide range of scientific and engineering disciplines. The focus of the conference is both on scientific libraries and tools developed with Python and on scientific or engineering achievements using Python. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission of Papers ==================== The program features tutorials, contributed papers, lightning talks, and bird-of-a-feather sessions. We are soliciting talks and accompanying papers (either formal academic or magazine-style articles) that discuss topics which center around scientific computing using Python. These include applications, teaching, future development directions, and research. A collection of peer-reviewed articles will be published as part of the proceedings. Proposals for talks are submitted as extended abstracts. There are two categories of talks: Paper presentations These talks are 35 minutes in duration (including questions). A one page abstract of no less than 500 words (excluding figures and references) should give an outline of the final paper. Proceeding papers are due two weeks after the conference, and may be in a formal academic style, or in a more relaxed magazine-style format. Rapid presentations These talks are 10 minutes in duration. An abstract of between 300 and 700 words should describe the topic and motivate its relevance to scientific computing. In addition, there will be an open session for lightning talks during which any attendee willing to do so is invited to do a couple-of-minutes-long presentation. If you wish to present a talk at the conference, please create an account on the website (http://conference.scipy.org). You may then submit an abstract by logging in, clicking on your profile and following the "Submit an abstract" link. Submission Guidelines --------------------- * Submissions should be uploaded via the online form. * Submissions whose main purpose is to promote a commercial product or service will be refused. * All accepted proposals must be presented at the SciPy conference by at least one author. * Authors of an accepted proposal can provide a final paper for publication in the conference proceedings. Final papers are limited to 7 pages, including diagrams, figures, references, and appendices. The papers will be reviewed to help ensure the high-quality of the proceedings. For further information, please visit the conference homepage: http://conference.scipy.org. Important Dates =============== * Friday, June 26: Abstracts Due * Saturday, July 4: Announce accepted talks, post schedule * Friday, July 10: Early Registration ends * Tuesday-Wednesday, August 18-19: Tutorials * Thursday-Friday, August 20-21: Conference * Saturday-Sunday, August 22-23: Sprints * Friday, September 4: Papers for proceedings due Tutorials ========= Two days of tutorials to the scientific Python tools will precede the conference. There will be two tracks: one for introduction of the basic tools to beginners and one for more advanced tools. Tutorials will be announced later. Birds of a Feather Sessions =========================== If you wish to organize a birds-of-a-feather session to discuss some specific area of scientific development with Python, please contact the organizing committee. Executive Committee =================== * Jarrod Millman, UC Berkeley, USA (Conference Chair) * Gaël Varoquaux, INRIA Saclay, France (Program Co-Chair) * Stéfan van der Walt, University of Stellenbosch, South Africa (Program Co-Chair) * Fernando Pérez, UC Berkeley, USA (Tutorial Chair)

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