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Charming Python: Using state machinesAlgorithms and programming approaches in Python

> Is there a way to find the number of processors on a machine (on linux/ > windows/macos/cygwin) using python code (using the same code/cross > platform code)? From processing <http://cheeseshop.python.org/pypi/processing/0.34> : def cpuCount():     '''     Returns the number of CPUs in the system     '''     if sys.platform == 'win32':         try:             num = int(os.environ['NUMBER_OF_PROCESSORS'])         except (ValueError, KeyError):             pass     elif sys.platform == 'darwin':         try:             num = int(os.popen('sysctl -n hw.ncpu').read())         except ValueError:             pass     else:         try:             num = os.sysconf('SC_NPROCESSORS_ONLN')         except (ValueError, OSError, AttributeError):             pass     if num >= 1:         return num     else:         raise NotImplementedError --

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.

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.

Pyro is short for PYthon Remote Objects. It is an advanced and powerful Distributed Object Technology system written entirely in Python, that is designed to be very easy to use. Never worry about writing network communication code again, when using Pyro you just write your Python objects like you would normally. With only a few lines of extra code, Pyro takes care of the network communication between your objects once you split them over different machines on the network. All the gory socket programming details are taken care of, you just call a method on a remote object as if it were a local object!