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There are two applications that require using the NumPy array type in C extension modules: Access to numerical libraries: Extension modules can be used to make numerical libraries written in C (or languages linkable to C, such as Fortran) accessible to Python programs. The NumPy array type has the advantage of using the same data layout as arrays in C and Fortran. Mixed-language numerical code: In most numerical applications, only a small part of the total code is CPU time intensive. Only this part should thus be written in C, the rest can be written in Python. NumPy arrays are important for the interface between these two parts, because they provide equally simple access to their contents from Python and from C. This document is a tutorial for using NumPy arrays in C extensions.

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.

 > 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.

Description geopy makes it easy for developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other sources of data, such as wikis. Development Status Under development since September 2006. Latest release: 0.93. License geopy is open source software released under the MIT license. Contact Questions, comments, and bug reports are welcome at exogen@gmail.com, the geopy mailing list, and on brian's blog.

For example edge labels can be added using matplotlib "text" objects like this: import networkx as nx import pylab as plot K=nx.XGraph(name="Konigsberg", multiedges=True, selfloops=False) K.add_edges_from([("A","B","Honey Bridge"),     ("A","B","Blacksmith's Bridge"),     ("A","C","Green Bridge"),     ("A","C","Connecting Bridge"),     ("A","D","Merchant's Bridge"),     ("C","D","High Bridge"),     ("B","D","Wooden Bridge")]) pos=nx.spring_layout(K) nx.draw_nx(K,pos) xa,ya=pos['A'] xb,yb=pos['B'] plot.text((xa+xb)/2,(ya+yb)/2,"Blacksmith's Bridge") plot.show() With a little work you can get the label rotated and exactly how you want it positioned.  You can also set the node positions directly in the "pos" dictionary above.

PyNGL (pronounced "pingle") is a Python language module used to visualize scientific data, with an emphasis on high quality 2D visualizations. A working knowledge of Python is assumed.

PyACTS- http://www.pyacts.org -- PyACTS is a collection of Python Modules that are very useful to Parallel Computing in a High Performance Computing environment. This packages incorporates several modules like PyBLACS (allows communication data for Linear Algebra), PyPBLAS (distributed Matrix Operations) and PyScaLAPACK (solve linear systems and get the eigenvalue problems). These libraries are part of PyACTS project that provide interfaces to the ACTS Collection. Also is provided a parrallel interpreter for using this package that implements message-based parallel programming using MPI.

PyScripter is a free and open-source Python Integrated Development Environment (IDE) created with the ambition to become competitive in functionality with commercial Windows-based IDEs available for other languages. Being built in a compiled language is rather snappier than some of the other Python IDEs and provides an extensive blend of features that make it a productive Python development environment. 

I love python for its beautiful code and practicality. But it’s not going to win a pure speed race with most languages. Most people think of speed and ease-of-use as polar opposites - probably because they remember the pain of writing C. Cython tries to eliminate that duality and lets you have python syntax with C data types and functions - the best of both worlds. Keeping in mind that I’m by no means an expert at this, here are my notes based on my first real experiment with Cython:

Linda is an widely studied distributed computing environment, centered around the notion of a tuple space. A tuple space is a bag (also called a multi-set) of tuples. A tuple is an ordered, typed chunk of data. Tuple spaces exist independently of processes in the system, and the data placed into a tuple space also exist independently. See "Generative communication in Linda" (1985) and "Multiple tuple spaces in Linda" both by David Gelernter for more information on Linda.