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python-ldap-2.4.6-3.fc18.armv6hl: python-ldap provides an object-oriented API for working with LDAP within Python programs. It allows access to LDAP directory servers by wrapping the OpenLDAP 2.x libraries, and contains modules for other LDAP-related tasks (including processing LDIF, LDAPURLs, LDAPv3 schema, etc.).

python-jinja2-2.6-1.lbn13.noarch: Jinja2 is a template engine written in pure Python. It provides a Django inspired non-XML syntax but supports inline expressions and an optional sandboxed environment. If you have any exposure to other text-based template languages, such as Smarty or Django, you should feel right at home with Jinja2. It's both designer and developer friendly by sticking to Python's principles and adding functionality useful for templating environments.

python-ipdb-0.7-1.lbn13.noarch: ipdb exports functions to access the IPython debugger, which features tab completion, syntax highlighting, better tracebacks, better introspection with the same interface as the pdb module. Example usage: import ipdb ipdb.set_trace() ipdb.pm() ipdb.run('x[0] = 3') result = ipdb.runcall(function, arg0, arg1, kwarg='foo') result = ipdb.runeval('f(1,2) - 3') The post-mortem function, ipdb.pm(), is equivalent to the magic function %debug. If you install ipdb with a tool which supports setuptools entry points, an ipdb script is made for you. You can use it to debug your scripts like $ bin/ipdb mymodule.py With Python 2.7 only, you can also use $ python -m ipdb mymodule.py You can also enclose code with the with statement to launch ipdb if an exception is raised: from ipdb import launch_ipdb_on_exception with launch_ipdb_on_exception(): [...]

python-interlude-1.0-2.lbn13.noarch: Interlude for Doctests provides an Interactive Console.

python-initgroups-2.13.0-2.lbn13.x86_64: Convenience uid/gid helper function used in Zope2.

python-imaging-1.1.7-7.fc18.armv6hl: Python Imaging Library The Python Imaging Library (PIL) adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and powerful image processing capabilities. Notice that in order to reduce the package dependencies there are three subpackages: devel (for development); tk (to interact with the tk interface) and sane (scanning devices interface).

python-imaging-1.1.6-17.fc12.x86_64: Python Imaging Library The Python Imaging Library (PIL) adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and powerful image processing capabilities. Notice that in order to reduce the package dependencies there are three subpackages: devel (for development); tk (to interact with the tk interface) and sane (scanning devices interface).

python-icalendar-3.4-2.lbn13.noarch: iCalendar specification (RFC 2445) defines calendaring format used by many applications (Zimbra, Thunderbird and others). This module is a parser/generator of iCalendar files for use with Python. It follows the RFC 2445 (iCalendar) specification. The aim is to make a package that is fully compliant with RFC 2445, well designed, simple to use and well documented.

python-i18ndude-3.2.2-2.lbn13.noarch: i18ndude performs various tasks related to ZPT's, Python Scripts and i18n.

python-htmllaundry-2.0-1.lbn13.noarch: This package contains several handy python methods to cleanup HTML markup or perform other common changes. The cleanup is strict enough to be able to clean HTML pasted from MS Word or Apple Pages. This package also contains integration code for z3c.form to provide fields that automatically sanitize HTML on save. The implementation is based on the Cleaner class from lxml.

python-html5lib-0.90-2.lbn13.noarch: html5lib

python-hachoir-parser-1.2.1-1.lbn13.noarch: README updated. s a package of most common file format parsers written for Hachoir framework. Not all parsers are complete, some are very good and other are poor: only parser first level of the tree for example. A perfect parser have no "raw" field: with a perfect parser you are able to know each bit meaning. Some good (but not perfect ;-)) parsers: * Matroska video * Microsoft RIFF (AVI video, WAV audio, CDA file) * PNG picture * TAR and ZIP archive What's new in hachoir-parser 1.2.1? * Improve OLE2 and MS Office parsers: - support small blocks - fix the charset of the summary properties - summary property integers are unsigned - use TimedeltaWin64 for the TotalEditingTime field - create minimum Word document parser * Python parser: support magic numbers of Python 3000 with the keyword only arguments * Create Apple/NeXT Binary Property List (BPLIST) parser * MPEG audio: reject file with no valid frame nor ID3 header * Skip subfiles in JPEG files * Create Apple/NeXT Binary Property List (BPLIST) parser by Robert Xiao

python-hachoir-metadata-gtk-1.2.1-3.lbn13.noarch: Desktop ui for hachoir-metadata command line

python-hachoir-metadata-1.2.1-3.lbn13.noarch: hachoir-metadata extracts metadata from multimedia files: music, picture, video, but also archives. It supports most common file formats: * Archives: bzip2, gzip, zip, tar * Audio: MPEG audio ("MP3"), WAV, Sun/NeXT audio, Ogg/Vorbis (OGG), MIDI, AIFF, AIFC, Real audio (RA) * Image: BMP, CUR, EMF, ICO, GIF, JPEG, PCX, PNG, TGA, TIFF, WMF, XCF * Misc: Torrent * Program: EXE * Video: ASF format (WMV video), AVI, Matroska (MKV), Quicktime (MOV), Ogg/Theora, Real media (RM) It tries to give as much information as possible. For some file formats, it gives more information than libextractor for example, such as the RIFF parser, which can extract creation date, software used to generate the file, etc. But hachoir-metadata cannot guess informations. The most complex operation is just to compute duration of a music using frame size and file size. hachoir-metadata has three modes: * classic mode: extract metadata, you can use --level=LEVEL to limit quantity of information to display (and not to extract) * --type: show on one line the file format and most important informations * --mime: just display file MIME type The command 'hachoir-metadata --mime' works like 'file --mime', and 'hachoir-metadata --type' like 'file'. But today file command supports more file formats then hachoir-metadata.

python-hachoir-core-1.2.1-1.lbn13.noarch: Hachoir project Hachoir is a Python library used to represent of a binary file as a tree of Python objects. Each object has a type, a value, an address, etc. The goal is to be able to know the meaning of each bit in a file. Why using slow Python code instead of fast hardcoded C code? Hachoir has many interesting features: * Autofix: Hachoir is able to open invalid / truncated files * Lazy: Open a file is very fast since no information is read from file, data are read and/or computed when the user ask for it * Types: Hachoir has many predefined field types (integer, bit, string, etc.) and supports string with charset (ISO-8859-1, UTF-8, UTF-16, ...) * Addresses and sizes are stored in bit, so flags are stored as classic fields * Endian: You have to set endian once, and then number are converted in the right endian * Editor: Using Hachoir representation of data, you can edit, insert, remove data and then save in a new file.

python-greenlet-0.3.4-2.lbn13.x86_64: Lightweight in-process concurrent programming

python-greenlet-0.3.1-4.fc17.armv6hl: The greenlet package is a spin-off of Stackless, a version of CPython that supports micro-threads called "tasklets". Tasklets run pseudo-concurrently (typically in a single or a few OS-level threads) and are synchronized with data exchanges on "channels".

python-gocept.munin-0.1-3.lbn13.noarch: This package provides base classes for defining Munin graphs and a main function to handle munin-typical symlinked scripts. Munin plugin scripts ==================== A single plugin file defines (multiple) graphs by subclassing the bases as mentioned in the next section. The main function uses the called script's filename to determine which of the defined graphs is relevant. Data for each graph is determined by retrieving a text file from a URL (with possibly given basic authentication data). The format for the data is plain/text wich each line having key/value pairs split by ':'. The values are expected to be floats. All graph bases expect two environment variables to be set: URL The URL at which to retrieve data from. Must include a '%s' which encodes the graph name. AUTH HTTP basic authentication information. Either empty or in the form of 'username:password'. The environment variables can be configured using munin's `plugin-conf.d` like this: The plugin is called `prefix_something_1`:: [prefix_*] env.URL http://foo:8900/myapp/munin?data=%s env.AUTH admin:admin Graph bases =========== The following base classes are currently defined: SimpleGraph A simple graph with a single value that is plotted as an absolute value. SimpleMultiGraph Multiple absolute values plotted on the same graph and scale. more bases will be added as needed. Main function ============= The main function handles munin-typical `type_option_index` symlink scripts by looking at the name from which the main script was called. Example script ============== Here's a sample script that you could symlink into the `plugins` directory:: from gocept.munin.client import SimpleGraph, main class people(SimpleGraph): name = key = 'people' title = 'How many people are there?' category = 'Office' main() [root@mistress gocept.munin-0.1]# cat README.txt ========================================== Utilities for writing munin client scripts ========================================== This package provides base classes for defining Munin graphs and a main function to handle munin-typical symlinked scripts. Munin plugin scripts ==================== A single plugin file defines (multiple) graphs by subclassing the bases as mentioned in the next section. The main function uses the called script's filename to determine which of the defined graphs is relevant. Data for each graph is determined by retrieving a text file from a URL (with possibly given basic authentication data). The format for the data is plain/text wich each line having key/value pairs split by ':'. The values are expected to be floats. All graph bases expect two environment variables to be set: URL The URL at which to retrieve data from. Must include a '%s' which encodes the graph name. AUTH HTTP basic authentication information. Either empty or in the form of 'username:password'. The environment variables can be configured using munin's `plugin-conf.d` like this: The plugin is called `prefix_something_1`:: [prefix_*] env.URL http://foo:8900/myapp/munin?data=%s env.AUTH admin:admin Graph bases =========== The following base classes are currently defined: SimpleGraph A simple graph with a single value that is plotted as an absolute value. SimpleMultiGraph Multiple absolute values plotted on the same graph and scale. more bases will be added as needed. Main function ============= The main function handles munin-typical `type_option_index` symlink scripts by looking at the name from which the main script was called. Example script ============== Here's a sample script that you could symlink into the `plugins` directory:: from gocept.munin.client import SimpleGraph, main class people(SimpleGraph): name = key = 'people' title = 'How many people are there?' category = 'Office' main()

python-gocept-0.1-3.lbn13.noarch: gocept python module

python-gflags-1.5.1-4.lbn13.noarch: This project is the python equivalent of google-gflags, a Google commandline flag implementation for C++. It is intended to be used in situations where a project wants to mimic the command-line flag handling of a C++ app that uses google-gflags, or for a Python app that, via swig or some other means, is linked with a C++ app that uses google-gflags. The gflags package contains a library that implements commandline flags processing. As such it's a replacement for getopt(). It has increased flexibility, including built-in support for Python types, and the ability to define flags in the source file in which they're used. (This last is its major difference from OptParse.)

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