PUBLIC MARKS with tags python & bibliotheques

A few years ago I «fell in love» with Python , which is a dynamically typed interactive, object oriented scripting language. With a few extensions I found it very suitable for efficient visualization and problem solving in Scientific computing. So can it replace Matlab? For me its pretty close! For you? It depends on your needs, but have a look! Why I use Python * Python is a small, high level scripting language that sits on top of a efficient C library. Because of this, Python code is compact, and the resulting code can run at a speed close to C if the computationally intensive parts are done via library calls. * Short learning curve - I was almost instantly productive. * Python can be used interactively (like matlab), and documentation for most functions can be accessed via a built in help facility. * It is free (also in this regard) * The syntax invites you to write clean code. No ;'s at the end of lines, the block structure is described by indentation instead of Begin-End or {..}. Through the Numeric/numarray modules one gets powerful array syntax - inspired by languages such as Fortran 90, Matlab, Octave, Yorick etc. Python itself has also borrowed features from e.g. Lisp, with its interactivity and built in support for list manipulation. * Python has many other useful modules built in, one may for instance write a web server in just a few lines of code or work transparently with gzipped files (handy for analyzing large ascii data files) * Linking in and reusing Fortran subroutines is very easy using e.g. f2py mentioned below, or the Pyfort module found on www.python.org. Integration with C is of course even tighter since the most popular python is written in C. (yes. there is a java python...) * It is possible to work in single precision, which is sufficient for most scientific purposes. This makes it easier to work with large datasets/arrays using only half the memory compared to e.g. matlab. As my basic setup I use Python with the following extensions: Numpy: a.k.a. Numeric python, contain the advanced array syntax, as well as powerful and commonly used functions that can be applied to the multi dimensional arrays. Pygist: Gist is a very fast graphics library for 2D and 3D plots written directly for X11, but also ported to Mac and Windows. Gist is a part of the Yorick language. Pygist contain the Python bindings, read about it here. A recent version of Pygist can be found here. Pygist is currently also a part of a distribution of Python packages called Scipy, that can be found here. f2py: Makes connecting Fortran subroutines a breeze! Also a part of Scipy. A complete example: wrap this subroutine in a Python function returning "dist": [avle@tindved test]$ cat r1.f90 subroutine r1(x,y,n,dist) real x(n),y(n) !f2py intent(out) dist xl=0.0 ; yl=0.0 ; vp=0.0 do i=1,n xl=xl + x(i)**2 ; yl=yl + y(i)**2 vp=vp + x(i)*y(i) end do if(vp>=0.0)then dist = acos(sqrt(vp/(xl*yl))) else dist = 4*atan(1.0)-acos(sqrt(-vp/(xl*yl))) end if end subroutine r1 [avle@tindved test]$ ls r1.f90 [avle@tindved test]$ f2py -c -m r1 --fcompiler=g95 r1.f90 ..lots of output... [avle@tindved test]$ ls r1.f90 r1.so* [avle@tindved test]$ python2 Python 2.2.3 (#1, Feb 15 2005, 02:41:06) [GCC 3.2.3 20030502 (Red Hat Linux 3.2.3-49)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import Numeric as nx, r1 >>> a=nx.array((2.3,2.2)) ; b=nx.array((3.2,2.1)) >>> r1.r1(a,b) 1.2827057838439941 >>>

pynetfilter_conntrack is a Python binding of libnetfilter_conntrack. The binding is the file pynetfilter_conntrack.py and you have also a clone of conntrack program: conntrack.py. See also pyctd project. What's this? ¶ This python library is based on libnetfilter_conntrack, which lets you manipulate conntrack objects. In other words, pynetfilter_conntrack lets you deal with Netfilter's stateful inspection objects from the Python world. Practically, for the administrator, this means you can now easily close connections of your choice on your Linux [2.6] firewall. You can also receive informations about all connections (how many packets have gone through, how many bytes, etc.). You will even be able to create new objects in the Connection Tracking (this means that complex protocols such as FTP, P2P, etc. can have Python dealing with them rather than complex kernel modules).

# Traits: Manifest typing for Python

The Python Packaging Index (the software formerly known as Cheeseshop) is now available at http://pypi.python.org/pypi The old addresses (www.python.org/pypi, and cheeseshop.python.org/pypi) will continue to work, either as aliases or using HTTP redirections. The software was renamed to its old name (PyPI - Python Package Index), as the Cheeseshop name was ever confusing people unfamiliar with British television comedy sketch (and puzzling even to people familiar with the sketch, as you *can* get packages from the package index). The Python Package Index is a repository of software for the Python programming language. There are currently 2609 packages here. You may:

PARLEY is an API for writing Python programs that implement the Actor model of distributed systems, in which lightweight concurrent processes communicate through asynchronous message-passing. Actor systems typically are easier to write and debug than traditional concurrent programs that use locks and shared memory. PARLEY can run using either traditional native threads, greenlets (lightweight threads), or Stackless Python's tasklets. A program written using PARLEY can choose between these models by changing a single line of code. Usage Messages in PARLEY can be arbitrary objects, but the standard message format is a 4-tuple: (tag, sender, args, kwargs). A typical way to handle such a message is to look up a function based on the tag; pass args and kwargs as parameters to the function (args being position parameters, and kwargs being keyword parameters); and to send the return value of the function as a message to the original sender. You can check out the latest version from Subversion using the following command: svn co http://artdent.homelinux.net/parley/trunk

multitask allows [WWW] Python programs to use [WWW] generators (aka coroutines) to perform cooperative multitasking and asynchronous I/O. Applications written using multitask consist of a set of cooperating tasks that yield to a shared task manager whenever they perform a (potentially) blocking operation, such as I/O on a socket or getting data from a queue. The task manager temporarily suspends the task (allowing other tasks to run in the meantime) and then restarts it when the blocking operation is complete. Such an approach is suitable for applications that would otherwise have to use select() and/or multiple threads to achieve concurrency. multitask is [WWW] free software, distributed under the [WWW] MIT license.