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Mpmath is a pure-Python library for multiprecision floating-point arithmetic. It provides an extensive set of transcendental functions, unlimited exponent sizes, complex numbers, interval arithmetic, numerical integration and differentiation, root-finding, linear algebra, and much more. Almost any calculation can be performed just as well at 10-digit or 1000-digit precision, and in many cases mpmath implements asymptotically fast algorithms that scale well for extremely high precision work. If available, mpmath will (optionally) use gmpy to speed up high precision operations.

The module comes with a demo application which is written in wxWidgets. The demo emulates any terminal program inside a text box. The following are the screenshots of emulating bash, emacs and vi using TermEmulator and the demo. successfully emulated(ran in a text box) bash, emacs, vi and some other terminal programs.

Provides callback for updating terminal screen

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:

The taskbar module is really experimental - and I think this is one of the first questions about it :)    tb.RunningApplication.Button(i).Click() - doesn't use ClickInput() 

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

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).

RRDTool is a really good back-end for storing time-series data. If you are developing tools that need a data repository and graphing capabilities, this provides you both. You create an RRD and then you begin inserting data values at regular intervals. You then call the graphing API to have a graph displayed. The neat thing about this data storage is its “round robin” nature. You define various time spans, and the granularity at which you want them stored. A fixed binary file is created, which never grows in size over time. As you insert more data, it is inserted into each span. As results are collected, they are averaged and rolled into successive time spans. It makes a much more efficient system than using your own complex data structures, relational database, or file system storage.

Easily create Google charts using python syntax and datasets. Python wrapper for the Google Chart API. The wrapper can render the URL of the Google chart, based on your parameters, or it can render an HTML img tag to insert into webpages on the fly. Made for dynamic python websites (Django,Zope,CGI,etc.) that need on the fly chart generation without any extra modules. The wrapper SHOULD also work with Eastwood the Google Chart API workalike. G = GChart('p3', [5,10])

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.

For drawing you can use pygraphviz (also available at networkx.lanl.gov) or the built-in drawing tools.

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.

lternative: if your file has indent 4 and you want an indent of 3 and gg=G is not working as expected, try :set inde=indent(v:lnum)/4*3 then go ahead with the well known gg=G use tabs or spaces, doesn't matter

ProfitPy is a set of libraries and tools for the development, testing, and execution of automated stock trading systems Specifically, ProfitPy is a collection of Python packages, modules, and scripts that work together to help you implement an automated stock trading program (or programs). The package features: A library with classes for accounts, orders, tickers, and more A GUI program for executing and plotting trades in real-time A GUI tool to collect ticker data for off-line use A CLI tool to back-test trading strategies Dozens of technical indicators and plotting widgets to display them An X11 keystroke tool for automating the TWS application

Charming Python: SimPy simplifies complex models

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.

Re: Kill process based on window name (win32) Thank you Roger. Your advice did the trick. For anyone interested, the basic code to terminate a process (politely) would be something like this (hwnd is retrieved using win32gui.EnumerateWindows): # Get the window's process id's t, p = win32process.GetWindowThreadProcessId(hwnd) # Ask window nicely to close win32gui.PostMessage(hwnd, win32con.WM_CLOSE, 0, 0) # Allow some time for app to close time.sleep(10) # If app didn't close, force close try: handle = win32api.OpenProcess(win32con.PROCESS_TERMINATE, 0, p) if handle: win32api.TerminateProcess(handle,0) win32api.CloseHandle(handle) except: pass:

### def reverse(l): return reduce(lambda x, y: [y] + x, [[]] + l) ###