Mailman 3 May 2003 - SciPy-User

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by Marcos May 10, 2003

May 10, 2003

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PyTables 0.5 released
by Francesc Alted May 10, 2003

May 10, 2003

Announcing PyTables 0.5 ----------------------- This is the second public beta release. On this release you will find a 20% of I/O speed improvement over the previous one (0.4), some bugs has been fixed and support for a couple of compression (LZO and UCL) libraries has been added, and... a long awaited Windows version is finally available!. More in detail: What's new ----------- - As a consequence of some twiking the write/read performance has been improved by a 20% overall. One particular case were performance has largely increased (0.5 is up to 6 times faster than 0.4) is when column elements are unidimensional arrays. This impressive speed-up is mainly because of the recent improvements in numarray 0.5 performance (good work, folks!). With that, the reading speed is reaching its theoretical maximum (at least when using the current data access schema). - When reading a Table object, and the user wants to fetch column elements which are unidimensional arrays, a copy of the array from the I/O buffer is delivered automatically to him, so that there is no need to make a call to .copy() method of the numarray arrays anymore. It think this is more comfortable for the user. - The compression was enabled by default in version 0.4, despite of what was stated in the documentation. Now, this has been corrected and compression is *disabled* by default. - Support for two new compression libraries: LZO and UCL (http://www.oberhumer.com/opensource/). These libraries are made by Markus F.X.J. Oberhumer, and they stand for allowing *very* fast decompression. Now, if your data is compressible, you can obtain better reading speed than if not using compression at all!. The improvement is still more noticeable if your are dealing with extremely large (and compressible) data sets. Read the online documentation for more info about that: http://pytables.sourceforge.net/html-doc/usersguide-html3.html#subsection3.… - A couple of memory leaks has been isolated and fixed (it was hard, but I finally did it!). - A bug with column ordering of tables that happens in some special situations has been fixed (thanks to Stan Heckman for reporting this and suggesting the patch). - File class has now an 'isopen' attribute in order to check if a file is open or not. - Updated documentation, specially for giving advice about the use of the new compression libraries. See "Compression issues" subsection, (also on the web: http://pytables.sourceforge.net/html-doc/usersguide-html.html) - Added more unit tests (up to 218 now!) - PyTables has been tested against newest numarray 0.5 and it works just fine. It even works well with Python 2.3b1. - And last, but not least, a Windows version is available!. Thanks to Alan McIntyre for its porting!. There is even a binary ready for click and install. What it is ---------- In short, PyTables provides a powerful and very Pythonic interface to process and organize your table and array data on disk. Its goal is to enable the end user to manipulate easily scientific data tables and Numerical and numarray Python objects in a persistent hierarchical structure. The foundation of the underlying hierarchical data organization is the excellent HDF5 library (http://hdf.ncsa.uiuc.edu/HDF5). A table is defined as a collection of records whose values are stored in fixed-length fields. All records have the same structure and all values in each field have the same data type. The terms "fixed-length" and strict "data types" seems to be quite a strange requirement for an interpreted language like Python, but they serve a useful function if the goal is to save very large quantities of data (such as is generated by many scientific applications, for example) in an efficient manner that reduces demand on CPU time and I/O resources. Quite a bit effort has been invested to make browsing the hierarchical data structure a pleasant experience. PyTables implements just two (orthogonal) easy-to-use methods for browsing. What is HDF5? ------------- For those people who know nothing about HDF5, it is is a general purpose library and file format for storing scientific data made at NCSA. HDF5 can store two primary objects: datasets and groups. A dataset is essentially a multidimensional array of data elements, and a group is a structure for organizing objects in an HDF5 file. Using these two basic constructs, one can create and store almost any kind of scientific data structure, such as images, arrays of vectors, and structured and unstructured grids. You can also mix and match them in HDF5 files according to your needs. Platforms --------- I'm using Linux as the main development platform, but PyTables should be easy to compile/install on other UNIX machines. This package has also passed all the tests on a UltraSparc platform with Solaris 7 and Solaris 8. It also compiles and passes all the tests on a SGI Origin2000 with MIPS R12000 processors and running IRIX 6.5. With Windows, PyTables has been tested with Windows 2000 Professional SP1 and Windows XP, but it should also work with other flavors. An example? ----------- For online code examples, have a look at http://pytables.sourceforge.net/tut/tutorial1-1.html and http://pytables.sourceforge.net/tut/tutorial1-2.html Web site -------- Go to the PyTables web site for more details: http://pytables.sourceforge.net/ Share your experience --------------------- Let me know of any bugs, suggestions, gripes, kudos, etc. you may have. Have fun! -- Francesc Alted

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From NASTRAN to scipy
by Nils Wagner May 9, 2003

May 9, 2003

Hi all, I am looking for a scipy routine for reading NASTRAN (ASCII/binary) files ? Any hint would be appreciated Nils Relevant links http://www.sdtools.com/faq/FE06001.html http://www.mscsoftware.com/products/products_detail.cfm?S=74&PI=7&M=0

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May 9, 2003

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odeint, rtol, atol
by Arnd Baecker May 8, 2003

May 8, 2003

Hi, there seems to be a mismatch between documentation and implementation in scipy.integrate.odeint. According to the doc "rtol and atol can be either vectors the same length as y or scalars." However, for me the scalar variant does not work such that both rtol and atol have be to vectors. Below is an example which shows this for the one-dimensional case. Arnd # ------------------------------------------------------------------------- from Numeric import * from scipy.integrate import odeint y0=1.0 def f(y,t): return exp(-0.01*y*t) atol=1e-6 rtol=1e-6 t=arange(0, 3.0, 0.1) #y=odeint(f, y0, t) # ok #y=odeint(f, y0, t,atol=atol*ones(1),rtol=rtol*ones(1)) # ok y=odeint(f, y0, t,atol=atol,rtol=rtol) # error (segfault) print y # -------------------------------------------------------------------------

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BUSINESS PROPOSAL
by MPETI L.KABILA May 7, 2003

May 7, 2003

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scipy.xplt questions
by Arnd Baecker May 7, 2003

May 7, 2003

Hi, I have the following questions concerning scipy.xplt 1.) scipy.xplt.mouse When using several windows, is there a way to find out in which of these a user did a mouse click ? (An example for this type of situation is at the end of this mail.) 2.) is there a way to position the windows on the screen ? (preferably independent of Linux/Windows, if that is possible at all) For example, for X Windows I failed to find anything related to Xresources definitions). Something like this would be useful when opening several windows and would like these to appear at predefined locations. Arnd ##################################################### from scipy.xplt import * window(0) plg(sin(arange(0.0,1.0,0.1))) window(1) plg(cos(arange(0.0,1.0,0.1))) m=mouse(-1,0,"right click ends") while(m[9]!=3): print m m=mouse(-1,0,"right click ends")

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Weave callbacks with return values - an example
by Fernando Perez May 6, 2003

May 6, 2003

This is just a followup on my previous message, not a question. After Eric's reply, I needed my callback functions to return a value, and it turned out to be very simple to modify the code for that. I don't know how many people use weave, but for the benefit of those who do (and for google's archive in case future users need this), here's the example code. Cheers, f. #!/usr/bin/env python from weave import inline def foo(x,y): print "In Python's foo:" print 'x',x print 'y',y return x def cfoo(x,y): code = """ printf("Attemtping to call back foo() from C...\\n"); py::tuple foo_args(2); py::object z; // This will hold the return value of foo() foo_args[0] = x; foo_args[1] = y; z = foo.call(foo_args); printf("Exiting C code.\\n"); return_val = z; """ return inline(code,"foo x y".split() ) x=99 y="Hello" print "Pure python..." z=foo(x,y) print "foo returned:",z print "\nVia weave..." z=cfoo(x,y) print "cfoo returned:",z

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Another weave question
by Fernando Perez May 6, 2003

May 6, 2003

Hi all, I have again most of my old weave test examples working again (my cookbook for using weave), except for the one below. It used Py::Float before and worked fine, but now I can't find the magic incantation after the recent changes in the weave codebase. I searched the supplied weave examples and didn't find anything that could help me, so here I am again. If anyone is using weave, I'll be happy to share my examples file which has a bunch of small cases useful for different scenarios. <plug> In two weeks I'll give a talk about IPython, weave and MayaVi at the FrontRange Pythoneers meeting, I'll share the slides from that here when they are ready. URL: http://www.fr.co.us.pythoneers.org </plug> thanks in advance, Fernando. #----------------------------------------------------------------------------- # Here we return a list from the C code. This is probably *much* slower than # the python version, it's meant as an illustration and not as production # code. def cross_productC(a,b): """Cross product of two 3-d vectors. """ # py::tuple or py::list work equally well in this case. code = \ """ py::list cross(3); cross[0] = py::float (a(1)*b(2)-a(2)*b(1)); cross[1] = py::float (a(2)*b(0)-a(0)*b(2)); cross[2] = py::float (a(0)*b(1)-a(1)*b(0)); return_val = cross; """ return array(weave.inline(code,['a','b'], type_converters = converters.blitz))

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Re: [SciPy-user] callbacks in weave
by Eric Jones May 6, 2003

May 6, 2003

Close. You have to build an argument tuple, and the use the function object's call() method. The following should do the trick: from weave import inline def foo(x,y): print "In Python's foo:" print 'x',x print 'y',y def cfoo(x,y): code = """ printf("Attemtping to call back foo() from C...\\n"); py::tuple foo_args(2); foo_args[0] = x; foo_args[1] = y; foo.call(foo_args); printf("Done.\\n"); """ inline(code,"foo x y".split() ) eric Fernando Perez <fperez(a)colorado.edu> wrote .. > Hi all, > > I wonder if it's possible to call-back into python from weave-inlined code > snippets. The included tutorial makes a passing mention of callable objects, > but with no details. I tried the code below but failed to make much progress. > Any help would be greatly appreciated. (For reference, I'm using a fairly > recent CVS snapshot, from a week or two ago). > > Thanks in advance, > > Cheers, > > f. > > > # Code snippet below: > > from weave import inline > > def foo(x,y): > print "In Python's foo:" > print 'x',x > print 'y',y > > def cfoo(x,y): > code = """ > printf("Attemtping to call back foo() from C...\\n"); > foo(x,y); > printf("Done.\\n"); > """ > inline(code,"foo x y".split() ) > > x=99 > y="Hello" > > print "Pure python..." > foo(x,y) > print "Via weave..." > cfoo(x,y) > > _______________________________________________ > SciPy-user mailing list > SciPy-user(a)scipy.net > http://www.scipy.net/mailman/listinfo/scipy-user

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