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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.

Following my thoughts yesterday, here are some VIM python scripts to add python breakpoint and debugging features to VIM. With this set up the F7 key will set a breakpoint on a line of code, Shift-F7 will remove all breakpoints and Shift-F12 will execute a script in the python debugger. This only runs on windows as far as I know, because it uses the 'start' command to launch the debugger in a seperate process without VIM waiting for it to finish. This allows you to look through the source code (and fix it) while the debugging is still in progress.

What is Py++? Definition: Py++ is an object-oriented framework for creating a code generator for Boost.Python library. Py++ uses few different programming paradigms to help you to expose C++ declarations to Python. This code generator will not stand on your way. It will guide you through the whole process. It will raise warnings in the case you are doing something wrong with a link to the explanation. And the most important it will save your time - you will not have to update code generator script every time source code is changed.

Document options Document options requiring JavaScript are not displayed Rate this pageHelp us improve this contentLevel: IntroductoryDavid Mertz (mertz@gnosis.cx), Applied Metaphysician, Gnosis Software, Inc. 01 Mar 2001Although users usually think of Python as a procedural and object-oriented language, it actually contains everything you need for a completely functional approach to programming. This article discusses general concepts of functional programming, and illustrates ways of implementing functional techniques in Python. We'd better start with the hardest question: "What is functional programming (FP), anyway?" One answer would be to say that FP is what you do when you program in languages like Lisp, Scheme, Haskell, ML, OCAML, Clean, Mercury, or Erlang (or a few others). That is a safe answer, but not one that clarifies very much. Unfortunately, it is hard to get a consistent opinion on just what FP is, even from functional programmers themselves. A story about elephants and blind men seems apropos here. It is also safe to contrast FP with "imperative programming" (what you do in languages like C, Pascal, C++, Java, Perl, Awk, TCL, and most others, at least for the most part).