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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.)

python-gevent-0.13.8-2.lbn13.x86_64: gevent is a Python networking library that uses greenlet to provide synchronous API on top of libevent event loop. Features include: * Fast event loop based on libevent. * Lightweight execution units based on greenlet. * Familiar API that re-uses concepts from the Python standard library. * Cooperative sockets with ssl support. * DNS queries performed through libevent-dns. * Ability to use standard library and 3rd party modules written for standard blocking sockets * Fast WSGI server based on libevent-http. gevent is inspired by eventlet but features more consistent API, simpler implementation and better performance. Read why others use gevent and check out the list of the open source projects based on gevent. gevent is written and maintained by Denis Bilenko and is licensed under MIT license.

python-gevent-0.13.6-3.fc18.armv6hl: gevent is a coroutine-based Python networking library that uses greenlet to provide a high-level synchronous API on top of libevent event loop. Features include: * convenient API around greenlets * familiar synchronization primitives (gevent.event, gevent.queue) * socket module that cooperates * WSGI server on top of libevent-http * DNS requests done through libevent-dns * monkey patching utility to get pure Python modules to cooperate

python-gettext-1.2-2.lbn13.noarch: This implementation of Gettext for Python includes a Msgfmt class which can be used to generate compiled mo files from Gettext po files and includes support for the newer msgctxt keyword. The idea for this project had been rather ambitious, but never lived up to what is was supposed to do. Look at Babel (http://pypi.python.org/pypi/Babel) for a project more worthy of this projects name.

python-geojson-1.0.6-1.lbn13.noarch: This library contains: * Functions for encoding and decoding GeoJSON_ formatted data * Classes for all GeoJSON Objects * An implementation of the Python __geo_interface__ Specification

python-gdata-2.0.17-1.lbn13.noarch: This is a Python module for accessing online Google services such as: - Base - Blogger - Calendar - Health - Picasa Web Albums - Spreadsheets - Documents List - Contacts - YouTube - Google Apps Provisioning - Code Search - Notebook - Webmaster Tools API - Google Analytics Data Export API - Google Book Search Data API - Google Finance Portfolio Data API - Google Maps Data API - Sites Data API - Issue Tracker Data API

python-ftw-1.3.4-1.lbn13.noarch: ftw module

python-fpconst-0.7.3-6.fc12.noarch: This python module implements constants and functions for working with IEEE754 double-precision special values. It provides constants for Not-a-Number (NaN), Positive Infinity (PosInf), and Negative Infinity (NegInf), as well as functions to test for these values.

python-foolscap-0.4.2-3.fc12.noarch: Foolscap (aka newpb) is a new version of Twisted's native RPC protocol, known as 'Perspective Broker'. This allows an object in one process to be used by code in a distant process. This module provides data marshaling, a remote object reference system, and a capability-based security model.

python-flickrapi-1.4.2-4.lbn13.noarch: A Python module for interfacing with the Flickr API.

python-feedparser-5.0.1-1.lbn13.noarch: Universal Feed Parser is a Python module for downloading and parsing syndicated feeds. It can handle RSS 0.90, Netscape RSS 0.91, Userland RSS 0.91, RSS 0.92, RSS 0.93, RSS 0.94, RSS 1.0, RSS 2.0, Atom 0.3, Atom 1.0, and CDF feeds. It also parses several popular extension modules, including Dublin Core and Apple's iTunes extensions.

python-fastkml-0.5-1.lbn13.noarch: astkml is a library to read, write and manipulate kml files. The aims are to keep it simple and fast (using lxml if available). Fast refers to the time you spend to write and read KML files as well as the time you spend to get aquainted to the library or to create KML objects. It provides a subset of KML and is aimed at documents that can be read from multiple clients such as openlayers and google maps rather than to give you all functionality that KML on google earth provides. Geometries are handled as pygeoif or shapely (if installed) objects.

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