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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.

Sometimes you get an Excel spreadsheet (say, from the marketing departement) and you want to read tabular data from it (i.e. a line with column headers and lines of data). There are many ways to do this (including ODBC + mxODBC), but the easiest way I've found is this one : provide a file name and a sheet name, and read the data !

Your example works as you describe on recent matplotlib versions. I suspect you are using an old one. The preferred way of handling missing points in numpy, and therefore in matplotlib and pylab, however, is via masked arrays.import pylabimport numpy as npfrom numpy import maa = [1,2,3,4,5]b = np.array([6,2,np.nan,1,9])bm = ma.masked_where(np.isnan(b), b)pylab.plot(a,bm)pylab.show()There are many other examples of masked array use in the examples directory of the matplotlib distribution.EricFernando Abilleira wrote:> Dear sourceforge community,> > I come from a Matlab environment so I am used to plotting matrices that > contain NaN elements. This is very useful because in some cases one > doesn't have data for the entire matrix. If one tries plotting the data, > the NaN elements won't be plotted.

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.

Osh (Object SHell) is a tool that integrates the processing of structured data, database access, and remote access to a cluster of nodes. These capabilities are made available through a command-line interface (CLI) and a Python application programming interface (API). Osh processes streams of Python objects using simple commands. Complex data processing is achieved by command sequences in which the output from one command is passed to the input of the next. This is similar to composing Unix commands using pipes. However, Unix commands pass strings from one command to the next, and the commands (grep, awk, sed, etc.) are heavily string-oriented. Osh commands send primitive Python types such as strings and numbers; composite types such as tuples, lists and maps; objects representing files, dates and times; or even user-defined objects.

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:

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.

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

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.

pyPortMidi is a Python wrapper I wrote for PortMidi. PortMidi is a cross-platform C library for realtime MIDI control. Using pyPortMidi, you can send and receive MIDI data in realtime from Python.

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.