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sychotic is an innovative optimizing compiler for Python code. It has unique features, the most important of which is that it breaks through the ConstantTimeBarrier. It is very easy to use and has an interface similar to that of the popular Psyco project. Below, you can see a usage example. You can also learn about HowItWorks, the ProjectHistory and KnownIssues. You can also browse the easy-to-understand source especially the alysis.py analyzer module and the dingo.py runtime bootstrap. There is an introductory screencast available (less than 5 minutes long). Usage

Given Python's goal to be a powerful and easy-to-use scripting language, its features for working with dates and times are not as user-friendly as they should be. The purpose of pyfdate is to remedy that situation by providing features for working with dates and times that are as powerful and easy-to-use as the rest of Python.

If I have:     from sympy import Symbol, Integrate     x = Symbol('x')     f = x**2 + x     g = Integrate(f, x) how can I apply g to a numpy array? In other words, how can I "numpify" the g expression, injecting in it x = numpy.linspace(1, 9, 9)? What would be even nicer would be to be able to retrieve a lambda using numpy functions for g as a function of x (that way I don't have the overhead of numpifying it each time I want to apply it to an array).

PyProtocols extends the PEP 246 adapt() function with a new "declaration API" that lets you easily define your own protocols and adapters, and declare what adapters should be used to adapt what types, objects, or protocols.  In addition to its own Interface type, PyProtocols can also use Twisted and Zope's Interface types too.  (Of course, since Twisted and Zope interfaces aren't as flexible, only a subset Of the PyProtocols API works with them.  Specific limitations are listed in the documentation.)

I'm declaring this one to be the winner! (Not that my opinion counts for anythin, but I'm just impressed: a1, b1 = zip(*ab) (or a1, b1 = apply(zip,ab)?) BTW: this process isn't 100% inverse. It has the side effect of turning lists or strings (non-tuple sequences) into tuples, if they are supplied as the original sequences.

To attempt to explain it in the simplest terms, a continuation is a representation, at a particular point in a program, of everything the program is capable of doing subsequently. A continuation is a potential that depends on initial conditions. Rather than loop in a traditional way, it is possible to invoke the same continuation recursively with different initial conditions. One broad claim I have read is that continuations, in a theoretical sense, are more fundamental and underlie every other control structure. Don't worry if these ideas cause your brain to melt; that is a normal reaction.

In our last article on compression we showed you how to demonstrate run time encoding in Python. In this article we'll show you how to implement another kind of compression, Huffman encoding, which is useful when dealing with small sets of items, such as character strings.

On Sat, 15 Dec 2001 17:53:49 -0800 (PST), David Brady <daves_spam_dodging_account at yahoo.com> wrote : >More win32all questions... is it possible to get the >handle of the window that currently has the focus? >win32gui.GetActiveWindow() fails because I'm looking >for a window outside the process of my Python script. E:\>python ActivePython 2.1.1, build 212 (ActiveState) Python 2.1.1 (#20, Jul 26 2001, 11:38:51) [MSC 32 bit (Intel)] on win32 Type "copyright", "credits" or "license" for more information. >>> import win32gui >>> import time >>> for x in range(10): ... time.sleep(1) ... print x, win32gui.GetWindowText(win32gui.GetForegroundWindow()) ... 0 cmd - python 1 cmd - python 2 (Untitled) * SciTE 3 (Untitled) * SciTE 4 ActivePython Documentation 5 ActivePython Documentation 6 PythonWin 7 PythonWin 8 PythonWin

One of my recent research tasks required me to retrieve various information from del.icio.us, a well-known social bookmarking service. My programming language of choice is Python, and so I wrote a basic Python module for getting the data I needed.

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.

Speech Recognition may be possible with COM. I found an example of how its done in Python.

Looks like foom is busy. Anyway, here is the verbatim translation of the script foom linked. I can't test it myself as no mike is installed on my machine. If you hear "Starting Succeeded", then, try to say one/two/three. NEED CoHelper.ahk.

A 'Mathematica Notebook' styled interface to Python from within TeXmacs. Blocks of code can be run independently of their order, and full multiline blocks, i.e. function remain visible and easily modifiable.

Pypar does not require the Python interpreter to be modified or recompiled: Parallel python programs use the standard Python and need merely import the pypar module. This means for example that you can upgrade Python independently of your parallel codes.

Pypar is an efficient but easy-to-use module that allows programs/scripts written in the Python programming language to run in parallel on multiple processors and communicate using message passing. Pypar provides bindings to an important subset of the message passing interface standard MPI. Other Python MPI bindings available from other developers include: PyMPI, Scientific Python and pythonMPI.

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. 

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.

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.

SendKeys SendKeys(keys, pause = 0.05, with_spaces = False, with_tabs = False, with_newlines = False, turn_off_numlock = True) Parameters keys : str A string of keys. pause : float The number of seconds to wait between sending each key or key combination. with_spaces : bool Whether to treat spaces as {SPACE}. If False, spaces are ignored. with_tabs : bool Whether to treat tabs as {TAB}. If False, tabs are ignored. with_newlines : bool Whether to treat newlines as {ENTER}. If False, newlines are ignored. turn_off_numlock : bool Whether to turn off NUMLOCK before sending keys.

Key Code BACKSPACE {BACKSPACE}, {BS}, or {BKSP} BREAK {BREAK} CAPS LOCK {CAPSLOCK} or {CAP} DEL or DELETE {DELETE} or {DEL} DOWN ARROW {DOWN} END {END} ENTER {ENTER} or ~ ESC {ESC} HELP {HELP} HOME {HOME} INS or INSERT {INSERT} or {INS} LEFT ARROW {LEFT} NUM LOCK {NUMLOCK} PAGE DOWN {PGDN} PAGE UP {PGUP} PRINT SCREEN {PRTSC} RIGHT ARROW {RIGHT} SCROLL LOCK {SCROLLLOCK} SPACE BAR {PACE} TAB {TAB} UP ARROW {UP} F1 {F1} F2 {F2} F3 {F3} F4 {F4} F5 {F5} F6 {F6} F7 {F7} F8 {F8} F9 {F9} F10 {F10} F11 {F11} F12 {F12} F13 {F13} F14 {F14} F15 {F15} F16 {F16} F17 {F17} F18 {F18} F19 {F19} F20 {F20} F21 {F21} F22 {F22} F23 {F23} F24 {F24} Keypad add {ADD} Keypad subtract {SUBTRACT} Keypad multiply {MULTIPLY} Keypad divide {DIVIDE} Left Windows(R) {LWIN} Right Windows(R) {RWIN}

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.