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'tconfpy' is an advanced configuration file parser and validator for Python programs. By using 'tconfpy', Python programmers can provide their users with an external configuration file for setting program options, defining defaults, and so on. 'tconfpy' offloads the responsibility for parsing and validating a configuration file from the main application. The Python programmer need only deal with the results and any errors or warnings generated during the parsing process.

The zc.lockfile package provides a basic portable implementation of interprocess locks using lock files. The purpose if not specifically to lock files, but to simply provide locks with an implementation based on file-locking primitives. Of course, these locks could be used to mediate access to other files. For example, the ZODB file storage implementation uses file locks to mediate access to file-storage database files. The database files and lock file files are separate files.

pytest-localserver is a plugin for the pytest testing framework which enables you to test server connections locally. Sometimes monkeypatching urllib2.urlopen() just does not cut it, for instance if you work with urllib2.Request, define your own openers/handlers or work with httplib. In these cases it may come in handy to have an HTTP server running locally which behaves just like the real thing. Well, look no further!

These are a few data structures, classes and functions which we've needed over many years of Python programming and which seem to be of general use to other Python programmers. Many of the modules that have existed in pyutil over the years have subsequently been obsoleted by new features added to the Python language or its standard library, thus showing that we're not alone in wanting tools like these.

Pymacs is a powerful tool which, once started from Emacs, allows both-way communication between Emacs Lisp and Python. Pymacs aims Python as an extension language for Emacs rather than the other way around, and this asymmetry is reflected in some design choices. Within Emacs Lisp code, one may load and use Python modules. Python functions may themselves use Emacs services, and handle Emacs Lisp objects kept in Emacs Lisp space.

Some tools which enable multi-platform development on the TI Stellaris Launchpad boards. The Stellaris Launchpad is a low cost development board created by Texas Instruments that comes with an ARM Cortex-M4F processor. Included tools: lm4flash Command-line firmware flashing tool using libusb-1.0 to communicate with the Stellaris Launchpad ICDI. lmicdiusb TCP/USB bridge created by TI, letting GDB communicate with the Stellaris Launchpad ICDI.

Robot Framework is a generic test automation framework for acceptance testing and acceptance test-driven development (ATDD). It has easy-to-use tabular test data syntax and utilizes the keyword-driven testing approach. Its testing capabilities can be extended by test libraries implemented either with Python or Java, and users can create new keywords from existing ones using the same syntax that is used for creating test cases.

This package contains utilities used to package some of STScI's Python projects, specifically those projects that comprise stsci_python and Astrolib. It currently consists mostly of some setup_hook scripts meant for use with distutils2/packaging and/or d2to1, and a customized easy_install command meant for use with distribute. This package is not meant for general consumption, though it might be worth looking at for examples of how to do certain things with your own packages.

Pretty-print tabular data in Python. The main use cases of the library are: printing small tables without hassle: just one function call, formatting is guided by the data itself authoring tabular data for lightweight plain-text markup: multiple output formats suitable for further editing or transformation readable presentation of mixed textual and numeric data: smart column alignment, configurable number formatting, alignment by a decimal point

Tinyarrays are similar to NumPy arrays, but optimized for small sizes. Common operations on very small arrays are to 3-7 times faster than with NumPy (with NumPy 1.6 it used to be up to 35 times), and 3 times less memory is used to store them. Tinyarrays are useful if you need many small arrays of numbers, and cannot combine them into a few large ones. (The resulting code is still much slower than C, but it may now be fast enough.)