Mailman 3 March 2008 - SciPy-User

Sparse Matrix
by Natali Melgarejo Diaz 06 Mar '08

06 Mar '08

Good morning everyone, I have a problem with linsolve.spsolve, I am using it as a translation of teh operator "\" backslash in Matlab for division. CAm you tell if it's the same as \ in Matlab or if thre is another language. Normally, the parameters are going to be: A = sparse.lil_matrix(N,N) b = (K0**2 -KZ**2)*(-1j) then we add some values to A... Efd = linsolve.spsolve(A,b) + Ei And the values aren't the same as in Matkab., linsolve.spsolve(A,b) gets only zeros and when I plot i have a "singular martix" error. Thanks in advance for your help! -- ********Natali********

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scipy.stats.scoreatpercentile BUG ? (n, 1) shaped arrays versus (n, )
by eleanor 05 Mar '08

05 Mar '08

I've just tracked problems in my code down to this unexpected behaviour >>> import scipy >>> d = scipy.arange(1,100) >>> scipy.stats.scoreatpercentile(d,50) 50 That's correct Then shuffle the data >>> scipy.random.shuffle(d) >>> scipy.stats.scoreatpercentile(d,50) 50 Everything's still fine Now try it with an array where d.shape = (99,1) >>> d2 = d.reshape(-1,1) >>> d2.shape (99, 1) >>> scipy.stats.scoreatpercentile(d2,50) array([82]) What the!!! >>> d2[49] array([82]) The problem seems to be that the sort in scoreatpercentile values = np.sort(a) dosen't sort an (n, 1) array. I've had many problems with scipy and numpy functions not dealing well with (n,1) arrays when they expect (n,) or is it the other way round. Which shape should I use with 1D arrays, to lessen my temper tantrums? Eleanor

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help about imshow
by jose luis Lopez Martinez 05 Mar '08

05 Mar '08

my apologise for the previous mail in spanish well, my question is how show a image in scale of gray from 0 to 1?, because, my prgram is : from pylab import * from scipy import * H=array([[0.0,0.0,0.5,0.0,0.0],[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],[0.0,0.0,0.0,0.0,0.0]]) print H; figure() imshow(H,cmap=cm.gray) well, 0.5 show how white and it's not correct, because the 0.5 is the half the scale the gray thanks

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help about imshow
by jose luis Lopez Martinez 05 Mar '08

05 Mar '08

Saludos Como despliego una imagen con niveles de gris de 0 a 1?,porqe si le doy la siguiente informacion me despliega el 0.5 como si fuera el uno y no como la mitad de la escala de grises. from pylab import * from scipy import * H=array([[0.0,0.0,0.5,0.0,0.0],[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],[0.0,0.0,0.0,0.0,0.0]]) print H; figure() imshow(H,cmap=cm.gray) Asumo que el imshow realiza lo siguiente C=H.copy() C=C-C.min() C=C/C.max() figure() imshow(C,cmap=cm.gray) lo cual me da la misma imagen que la primera, es decir que el 0.5 lo toma como si fuera maximo valor de la escala de grises.Pero en realidad deberia desplegar visualmente un valor de gris intermedio y no el blanco(ya qu eel blanco solo lo debe mostrar si su valor es 1) gracias

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help about a solve sparse matrix
by jose luis Lopez Martinez 05 Mar '08

05 Mar '08

hi i work in my thesis of master and i will work with sparse matrix (i.e128^4), so my question is if exist a command in scipy that solve sparse matrix using at the Gauss-Seidel iteration method? thanks

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pynetcdf errors
by James Boyle 05 Mar '08

05 Mar '08

python - Python 2.5.1 numpy '1.0.3.1' pynetcdf 0.7 OS X 10.4.11 Using pynetcdf I create a file, write to it and then close the file. When the file closes I get a large number of the following messages: python(22656) malloc: *** Deallocation of a pointer not malloced: 0x239e6b0; This could be a double free(), or free() called with the middle of an allocated block; Try setting environment variable MallocHelp to see tools to help debug Any advice on ow to address this problem? I noticed that on the scipy user forum ( summer 2007) that there was a report of pynetcdf crashing on python 2.5.1 but there was no solution offered. --Jim

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scipy build with gcc on solaris problems
by John Reid 05 Mar '08

05 Mar '08

Hi, I'm on solaris using gcc: SunOS mahler 5.10 Generic_118833-36 sun4u sparc SUNW,Sun-Fire-880 Solaris Using built-in specs. Target: sparc-sun-solaris2.10 Configured with: ../configure --prefix=/usr/local --with-gnu-as --with-as=/usr/local/bin/as --with-gnu-ld --with-ld=/usr/local/bin/ld --with-libiconv --enable-libada --enable-libssp --enable-objc-gc --enable-threads --enable-languages=c,c++,objc,obj-c++,fortran Thread model: posix gcc version 4.2.2 The scipy tests generate a core dump. The numpy tests seem fine except for a few warnings about invalid values. What am I doing wrong? I build atlas following the advice here: http://www.scipy.org/Installing_SciPy/Linux#head-89e1f6afaa3314d98a22c79b06… I install scipy using "easy_install scipy" when I get some warnings like the following: scipy/ndimage/src/nd_image.c: In function 'Py_Filter1DFunc': scipy/ndimage/src/nd_image.c:273: warning: function called through a non-compatible type scipy/ndimage/src/nd_image.c:273: note: if this code is reached, the program will abort scipy/ndimage/src/nd_image.c:274: warning: function called through a non-compatible type scipy/ndimage/src/nd_image.c:274: note: if this code is reached, the program will abort scipy/ndimage/src/nd_image.c: In function 'Py_FilterFunc': scipy/ndimage/src/nd_image.c:351: warning: function called through a non-compatible type scipy/ndimage/src/nd_image.c:351: note: if this code is reached, the program will abort scipy/ndimage/src/nd_image.c: In function 'Py_Histogram': scipy/ndimage/src/nd_image.c:1100: warning: function called through a non-compatible type scipy/ndimage/src/nd_image.c:1100: note: if this code is reached, the program will abort and when I run the tests I get a core dump: ipython >>> import scipy >>> scipy.test() Here is the "gdb python core" output: Core was generated by `/usr/local/bin/python /usr/local/bin/ipython'. Program terminated with signal 4, Illegal instruction. #0 Py_FilterFunc (buffer=0xc91778, filter_size=2, output=0xffbfc118, data=0xffbfc1c4) at scipy/ndimage/src/nd_image.c:351 351 scipy/ndimage/src/nd_image.c: No such file or directory. in scipy/ndimage/src/nd_image.c (gdb) where #0 Py_FilterFunc (buffer=0xc91778, filter_size=2, output=0xffbfc118, data=0xffbfc1c4) at scipy/ndimage/src/nd_image.c:351 #1 0xfdc16eb8 in NI_GenericFilter (input=0x900708, function=0xfdc130b8 <Py_FilterFunc>, data=0xffbfc1c4, footprint=0xb96be0, output=0xb96c40, mode=<value optimized out>, cvalue=0, origins=0xbfcd60) at scipy/ndimage/src/ni_filters.c:858 #2 0xfdc14a54 in Py_GenericFilter (obj=0x0, args=0xb493f0) at scipy/ndimage/src/nd_image.c:411 #3 0x000f7a24 in PyCFunction_Call (func=0x838878, arg=0xb493f0, kw=0x0) at Objects/methodobject.c:108 #4 0x000a188c in PyEval_EvalFrameEx (f=0xbf6570, throwflag=-4209780) at Python/ceval.c:3564 #5 0x000a3784 in PyEval_EvalCodeEx (co=0x7f7b60, globals=0x7f7b60, locals=0x1, args=0x1a210c, argcount=2, kws=0x28, kwcount=3, defs=0x833dfc, defcount=8, closure=0x0) at Python/ceval.c:2831 #6 0x000a1804 in PyEval_EvalFrameEx (f=0xbf63c0, throwflag=-4209276) at Python/ceval.c:3659 #7 0x000a3784 in PyEval_EvalCodeEx (co=0x9fdf98, globals=0x9fdf98, locals=0x1, args=0x43d2b4, argcount=10432304, kws=0x20, kwcount=1, defs=0x0, defcount=8, closure=0x0) at Python/ceval.c:2831 #8 0x000a1804 in PyEval_EvalFrameEx (f=0xa4ee30, throwflag=-4208772) at Python/ceval.c:3659 #9 0x000a3784 in PyEval_EvalCodeEx (co=0x2bdf50, globals=0x2bdf50, locals=0x1, args=0x138800, argcount=2, kws=0x9db3c8, kwcount=0, defs=0x2d7c9c, defcount=1, closure=0x0) at Python/ceval.c:2831 #10 0x000f72ec in function_call (func=0x2dd2b0, arg=0x93c788, kw=0xb4b270) at Objects/funcobject.c:517 #11 0x00026448 in PyObject_Call (func=0x2dd2b0, arg=0x93c788, kw=0xb4b270) at Objects/abstract.c:1860 #12 0x0009f6f8 in PyEval_EvalFrameEx (f=0xa4ecc0, throwflag=0) at Python/ceval.c:3844 #13 0x000a3784 in PyEval_EvalCodeEx (co=0x2bdf98, globals=0x2bdf98, locals=0x1, args=0x138800, argcount=2, kws=0x0, kwcount=0, defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:2831 #14 0x000f72ec in function_call (func=0x2dd2f0, arg=0x93c620, kw=0x0) at Objects/funcobject.c:517 #15 0x00026448 in PyObject_Call (func=0x2dd2f0, arg=0x93c620, kw=0x0) at Objects/abstract.c:1860 #16 0x00031310 in instancemethod_call (func=0x2, arg=0x93c620, kw=0x0) at Objects/classobject.c:2497 #17 0x00026448 in PyObject_Call (func=0x8c7d78, arg=0x93c620, kw=0x0) at Objects/abstract.c:1860 #18 0x0009fe10 in PyEval_EvalFrameEx (f=0xa4eb40, throwflag=-4206524) at Python/ceval.c:3775 #19 0x000a3784 in PyEval_EvalCodeEx (co=0x2a35c0, globals=0x2a35c0, locals=0x1, args=0x138800, argcount=2, kws=0x0, kwcount=0, defs=0x61e25c, defcount=1, closure=0x0) at Python/ceval.c:2831 #20 0x000f72ec in function_call (func=0x67fdf0, arg=0x93c738, kw=0x0) at Objects/funcobject.c:517 #21 0x00026448 in PyObject_Call (func=0x67fdf0, arg=0x93c738, kw=0x0) at Objects/abstract.c:1860 #22 0x00031310 in instancemethod_call (func=0x0, arg=0x93c738, kw=0x0) at Objects/classobject.c:2497 #23 0x00026448 in PyObject_Call (func=0x8ce3f0, arg=0xaf9d70, kw=0x0) at Objects/abstract.c:1860 #24 0x00074ad8 in slot_tp_call (self=0xf, args=0xaf9d70, kwds=0x0) at Objects/typeobject.c:4633 #25 0x00026448 in PyObject_Call (func=0xa8de30, arg=0xaf9d70, kw=0x0) at Objects/abstract.c:1860 #26 0x0009fe10 in PyEval_EvalFrameEx (f=0xa57fa8, throwflag=-4204804) at Python/ceval.c:3775 #27 0x000a3784 in PyEval_EvalCodeEx (co=0x2d46e0, globals=0x2d46e0, locals=0x1, args=0x138800, argcount=2, kws=0x9db4f8, kwcount=0, defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:2831 #28 0x000f72ec in function_call (func=0x2dd730, arg=0x93c558, kw=0xb40e40) at Objects/funcobject.c:517 #29 0x00026448 in PyObject_Call (func=0x2dd730, arg=0x93c558, kw=0xb40e40) at Objects/abstract.c:1860 #30 0x0009f6f8 in PyEval_EvalFrameEx (f=0xa57e38, throwflag=0) at Python/ceval.c:3844 #31 0x000a3784 in PyEval_EvalCodeEx (co=0x2d4728, globals=0x2d4728, locals=0x1, args=0x138800, argcount=2, kws=0x0, kwcount=0, defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:2831 #32 0x000f72ec in function_call (func=0x2dd770, arg=0x942c10, kw=0x0) at Objects/funcobject.c:517 #33 0x00026448 in PyObject_Call (func=0x2dd770, arg=0x942c10, kw=0x0) at Objects/abstract.c:1860 #34 0x00031310 in instancemethod_call (func=0x0, arg=0x942c10, kw=0x0) at Objects/classobject.c:2497 #35 0x00026448 in PyObject_Call (func=0x759a58, arg=0x7d6bd0, kw=0x0) at Objects/abstract.c:1860 #36 0x00074ad8 in slot_tp_call (self=0xc, args=0x7d6bd0, kwds=0x0) at Objects/typeobject.c:4633 #37 0x00026448 in PyObject_Call (func=0x7bea30, arg=0x7d6bd0, kw=0x0) at Objects/abstract.c:1860 #38 0x0009fe10 in PyEval_EvalFrameEx (f=0x96e7a8, throwflag=-4202332) at Python/ceval.c:3775 #39 0x000a2b58 in PyEval_EvalFrameEx (f=0x95e978, throwflag=-4201972) at Python/ceval.c:3650 #40 0x000a3784 in PyEval_EvalCodeEx (co=0x2a3068, globals=0x2a3068, locals=0x1, args=0x610880, argcount=3, kws=0x18, kwcount=0, defs=0x6779fc, defcount=5, closure=0x0) at Python/ceval.c:2831 #41 0x000a1804 in PyEval_EvalFrameEx (f=0x78e3f8, throwflag=-4201468) at Python/ceval.c:3659 #42 0x000a3784 in PyEval_EvalCodeEx (co=0x60b188, globals=0x60b188, locals=0x1, args=0x1a2100, argcount=0, kws=0x8, kwcount=0, defs=0x774c44, defcount=2, closure=0x0) at Python/ceval.c:2831 #43 0x000a1804 in PyEval_EvalFrameEx (f=0x6baa30, throwflag=-4200964) at Python/ceval.c:3659 #44 0x000a3784 in PyEval_EvalCodeEx (co=0x775ba8, globals=0x775ba8, locals=0x1, args=0x0, argcount=0, kws=0x0, kwcount=0, defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:2831 #45 0x000a22e8 in PyEval_EvalFrameEx (f=0x804e78, throwflag=7822248) at Python/ceval.c:494 ---Type <return> to continue, or q <return> to quit--- #46 0x000a3784 in PyEval_EvalCodeEx (co=0x4e5020, globals=0x4e5020, locals=0x1, args=0x138800, argcount=2, kws=0x804e44, kwcount=0, defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:2831 #47 0x000a1804 in PyEval_EvalFrameEx (f=0x804cf0, throwflag=-4199956) at Python/ceval.c:3659 #48 0x000a3784 in PyEval_EvalCodeEx (co=0x4dcf50, globals=0x4dcf50, locals=0x1, args=0x2aa020, argcount=3, kws=0x10, kwcount=0, defs=0x58d424, defcount=2, closure=0x0) at Python/ceval.c:2831 #49 0x000a1804 in PyEval_EvalFrameEx (f=0x5d7aa0, throwflag=-4199452) at Python/ceval.c:3659 #50 0x000a2b58 in PyEval_EvalFrameEx (f=0x5e43c0, throwflag=-4199092) at Python/ceval.c:3650 #51 0x000a3784 in PyEval_EvalCodeEx (co=0x4dccc8, globals=0x4dccc8, locals=0x1, args=0x138800, argcount=2, kws=0x5df1f8, kwcount=0, defs=0x5901fc, defcount=1, closure=0x0) at Python/ceval.c:2831 #52 0x000a1804 in PyEval_EvalFrameEx (f=0x5df0b0, throwflag=-4198588) at Python/ceval.c:3659 #53 0x000a3784 in PyEval_EvalCodeEx (co=0x4dcbf0, globals=0x4dcbf0, locals=0x1, args=0x138800, argcount=2, kws=0x37c9fc, kwcount=0, defs=0x5901dc, defcount=1, closure=0x0) at Python/ceval.c:2831 #54 0x000a1804 in PyEval_EvalFrameEx (f=0x37c8b0, throwflag=-4198084) at Python/ceval.c:3659 #55 0x000a3784 in PyEval_EvalCodeEx (co=0x448410, globals=0x448410, locals=0x1, args=0x13796c, argcount=1, kws=0xc, kwcount=0, defs=0x358b04, defcount=2, closure=0x0) at Python/ceval.c:2831 #56 0x000a1804 in PyEval_EvalFrameEx (f=0x5d7338, throwflag=-4197580) at Python/ceval.c:3659 #57 0x000a3784 in PyEval_EvalCodeEx (co=0x428d10, globals=0x428d10, locals=0x1, args=0x13796c, argcount=0, kws=0x4, kwcount=0, defs=0x43d87c, defcount=1, closure=0x0) at Python/ceval.c:2831 #58 0x000a1804 in PyEval_EvalFrameEx (f=0x216480, throwflag=-4197076) at Python/ceval.c:3659 #59 0x000a3784 in PyEval_EvalCodeEx (co=0x1d1578, globals=0x1d1578, locals=0x1, args=0x0, argcount=0, kws=0x0, kwcount=0, defs=0x0, defcount=0, closure=0x0) at Python/ceval.c:2831 #60 0x000a3918 in PyEval_EvalCode (co=0x1d1578, globals=0x176d20, locals=0x176d20) at Python/ceval.c:494 #61 0x000c8c6c in PyRun_FileExFlags (fp=0x0, filename=0xffbffa52 "/usr/local/bin/ipython", start=-4, globals=0x176d20, locals=0x176d20, closeit=1, flags=0xffbff814) at Python/pythonrun.c:1271 #62 0x000c9ba8 in PyRun_SimpleFileExFlags (fp=0x15c4e8, filename=0xffbffa52 "/usr/local/bin/ipython", closeit=1, flags=0xffbff814) at Python/pythonrun.c:877 #63 0x0001de58 in Py_Main (argc=2, argv=0x0) at Modules/main.c:523 #64 0x0001d440 in _start ()

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Re: [SciPy-user] handling of huge files for post-processing
by Christoph Scheit 05 Mar '08

05 Mar '08

Hello David, indeed data in file a depends on data in file b... that the biggest problem and consequently I guess I need something that operates better on the file-system than in main memory. Do you think, it's possible to use PyTables to tackle the problem? I would need something that can group together such enormous data-sets. sqlite is nice to group data of a table together, but I guess my data-sets are just to big... Acutally I unfortunately don't see the possibility to iterate over the entries of the files in the manner you described below.... Thanks, Christoph ------------------------------ Message: 3 Date: Tue, 26 Feb 2008 09:17:00 -0500 From: "David Huard" <david.huard(a)gmail.com> Subject: Re: [SciPy-user] handling of huge files for post-processing To: "SciPy Users List" <scipy-user(a)scipy.org> Message-ID: <91cf711d0802260617o4d768824wbf5fae702b59f00a(a)mail.gmail.com> Content-Type: text/plain; charset="iso-8859-1" Cristoph, Do you mean that b depends on the entire dataset a ? In this case, you might consider buying additional memory; this is often way cheaper in terms of time than trying to optimize the code. What I mean by iterators is that when you open a binary file, you generally have the possibility to iterate over each element in the file. For instance, when reading an ascii file: for line in f.readline(): some operation on the current line. instead of loading all the file in memory: lines = f.readlines() This way, only one line is kept in memory at a time. If you can write your code in this manner, this might solve your memory problem. For instance, here is a generator that opens two files and will return the current line of each file each time it's next() method is called def read(): a = open('filea', 'r') b = open('fileb', 'r') la = a.readline() lb = b.readline() while (la and lb): yield la,lb la = a.readline() lb = b.readline() for a, b in read(): some operation on a,b HTH, David

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Control Systems Toolbox
by Jasper Stolte 04 Mar '08

04 Mar '08

Hi guys, I'm new to the list, nice to meet you all. Anyway my question is: Is there already someone developing some sort of Control Systems Toolbox / Robust Control Toolbox equivalent for SciPy? I would love to see it added, and I am thinking of building something from scratch. Obviously that wouldn't make much sense if other people are already working on something similar. Regards, Jasper

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[Newbie] High-performance plotting of large datasets
by scipy-user＠onnodb.com 04 Mar '08

04 Mar '08

Hi all, Using LabVIEW software for our data analysis at the moment, I'm currently looking for alternatives. Especially since LabVIEW's "graphical" programming language is somewhat cumbersome for some of the things we're doing --- an iterative language would often be much easier. (Actually, I personally don't like the graphical way of programming at all :) ) Python seems to be a great alternative, although I haven't been able yet to get things up & running the way I'd like. The main 'problem' is that LabVIEW contains a lot of high-performance library code for plotting data. I've been experimenting with SciPy and matplotlib, but those libraries are just *way* slower than LabVIEW when plotting large data sets (in our case, it's a current trace with a few MBs of data). I'd like to plot a current trace, so that the user can quickly zoom in & out, and pan using a horizontal scroll bar, but how should I do this? (I've looked around for examples a bit, but being a newbie, it can be hard to find your way around such a huge community). Another issue appears to be the creation of simple user interfaces. This is very intuitive in LabVIEW, but could someone here give some advice on a way to combine a UI and plot windows in a not-so-difficult to learn way in Python? What's your own experience? I've spent some time on looking at various packages and frameworks like Traits, Chaco and Envisage, but I just can't seem to wrap my head around them. I'm looking forward to any help; thank you very much in advance! Best regards, -- Onno Broekmans

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