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UliPad uses Mixin and Plugin technique as its architecture. Most of its classes can be extended via mixin and plugin components, and finally become an integrity class when creating the instance. So UliPad is very dynamic. You can write the new features in new files, and hardly need to modify the existing code. And if you want to extend the existing classes, you could write mixins and plugins, and this will be bound to the target class that I call "Slot Class". This technique will make the changes centralized and easily managed.

 > According to the fact that all Thread run on the same CPU (if i didn't  > understand wrong), i'm asking if python will suffer from the future  > multicore CPU. Will not python use only one core, then a half or a  > quarter of CPU ? It could be a serious problem for the future of python... I agree that it could potentially be a serious hindrance for cpython if "multiple core" CPUs become commonplace. This is in contrast to jython and ironpython, both of which support multiple-cpu parallelism. Although I completely accept the usual arguments offered in defense of the GIL, i.e. that it isn't a problem in the great majority of use cases, I think that position will become more difficult to defend as desktop CPUs sprout more and more execution pipelines. I think that this also fits in with AM Kuchling's recent musing/thesis/prediction that the existing cpython VM may no longer be in use in 5 years, and that it may be superceded by python "interpreters" running on top of other VMs, namely the JVM, the CLR, Smalltalk VM, Parrot, etc, etc, etc. http://www.amk.ca/diary/archives/cat_python.html#003382 I too agree with Andrew's basic position: the Python language needs a period of library consolidation. There is so much duplication of functionality out there, with the situation only getting worse as people re-invent the wheel yet again using newer features such generators, gen-exps and decorators.

What's the relation between these benefits and Stackless's implementation details? Here's a quick sketch: Continuations are the general-purpose concurrency construct. A continuation represents all the future computations of a particular program. Capturing all this control flow in a single conceptual object makes it programmable: It becomes possible to calculate or reason over the control flow. In particular, there's great scope for optimizing assignment of different calculations to different processes or threads or even hosts.

In short: run your existing Python software much faster, with no change in your source. Think of Psyco as a kind of just-in-time (JIT) compiler, a little bit like what exists for other languages, that emit machine code on the fly instead of interpreting your Python program step by step. The difference with the traditional approach to JIT compilers is that Psyco writes several version of the same blocks (a block is a bit of a function), which are optimized by being specialized to some kinds of variables (a "kind" can mean a type, but it is more general). The result is that your unmodified Python programs run faster. Benefits 2x to 100x speed-ups, typically 4x, with an unmodified Python interpreter and unmodified source code, just a dynamically loadable C extension module. Drawbacks Psyco currently uses a lot of memory. It only runs on Intel 386-compatible processors (under any OS) right now. There are some subtle semantic differences (i.e. bugs) with the way Python works; they should not be apparent in most programs.

The pyparsing module is an alternative approach to creating and executing simple grammars, vs. the traditional lex/yacc approach, or the use of regular expressions. The pyparsing module provides a library of classes that client code uses to construct the grammar directly in Python code.

PyCHM - Python bindings for CHMLIB PyCHM is a package that provides bindings for Jed Wing's CHMLIB library. The chm package contains four modules, namely chm.chm, chm.chmlib, chm.extra and chm._chmlib. chm.chmlib is a low level wrapper module around the API provided by the C library chmlib. Quoted from Jed's README: chmlib is a small library designed for accessing MS ITSS files. The ITSS file format is used for Microsoft Html Help files (.chm), which have been the predominant medium for software documentation from Microsoft during the past several years, having superceded the previously used .hlp file format. chm.chm provides some high level functionality over chm.chmlib, such as access to the .chm file contents tree. chm.extra contains extra functionality to allow detection encodings in the CHM archives and to support full-text search.

The pycurl package is a Python interface to libcurl (http://curl.haxx.se/libcurl/). pycurl has been successfully built and tested with Python versions from 2.2 to the current 2.5.x releases.

Here's what new-style classes have to offer: Properties: Attributes that are defined by get/set methods Static methods and class methods The new __getattribute__ hook, which, unlike __getattr__, is called for every attribute access, not just when the attribute can't be found in the instance Descriptors: A protocol to define the behavior of attribute access through objects Overriding the constructor __new__ Metaclasses (not discussed)

Metaclass programming in PythonPushing object-oriented programming to the next level

New to AOP? You may find this "Introduction to Aspect-Oriented Programming" (PDF) by Ken Wing Kuen Lee of the Hong Kong University of Science and Technology interesting.