Projects

Pardus Linux

Pardus is a GNU/Linux distribution developed by BTE/TUBITAK according to computer literates' basic desktop needs; uses existing distributions' dominant parts as concept, architecture or code; provides easy use, configuration, installation with configuration environment and tools that can be converted to an autonomous system.

Dina-Project

Dina is web framework that use Django as its base. Django did not provide some functionality that is important for web development ( because of its philosophy ). So Dina try to fill these gaps in Django and provide a higher level web framework on top of Django. Dina try to reach these goals: ... [More] * Be compatible with Django as much as it can. * Provide some CMS functionality to avoid extra coding * Provide a installer to easily install Dina * A powerful apt like package management to allow easy package installing * and etc ( for more information take a look at docs ). [Less]

OpenELEC - Media Center Operating System

OpenELEC is a GNU/Linux distro combined with XBMC to give you the best possible experience for watching you media. Including but not limited to tv-shows, movies, music, web-media, etc. It's built upon a less-is-more philosophy and aim to provide an all-in-one solution. Making you as the user ... [More] , more focused on the media you consume, rather than spending hour after hour to setup your environment. [Less]

VIPS

The VIPS library/image processing system is well suited for larger than RAM true and false-colour images. VIPS can be used for image format conversion, colour calibration, image filtering, transformation and analysis, thumbnail generation, small object recognition and many other image processing ... [More] tasks. VIPS is well suited for medical and scientific research & development and batch image processing. It is not so good for retouching photographs. The system has two main parts: libvips is the library, and nip2 is the GUI. Both execute common image processing tasks faster than other image processing systems because of sophisticated memory/task management and multicore compatibility. VIPS runs in batch (command line) mode on *nix, Windows, Mac and other OSes. [Less]

Genshi Compiler

Genshi Compiler allows for rendering your Genshi template to Python source code. You can save the code as a Python module or compile it into a directly usable module object in memory. Just call the render function on the module with your template parameters to render the whole template or any of ... [More] your template functions to render those fragments separately. According to my initial benchmarks the rendering speed is typically ~40x faster than doing the same using Genshi. There is a cost of this speedup, certainly. Some of Genshi's dynamic features are not available, most notably anything that depends on a template loader (xi:include), the XML element tree representation (py:match) or the token stream (filters). [Less]

poly2tri

An open source CDT library (sweepline), available in C++, C#, Java, and Python!

piti8rowser

Piti8rowser is a small,fast and smart web browser,explorer,viewer what do you want , that could it.

pyfmm

Developing an open-source Python implementation of the Fast Multipole Algorithm (FMM) for scientific applications.The FMM can be used in many scientific computing applications: the simulation of many stars, electrostatics, the calculation of atoms or molecules out of equilibrium, and particle ... [More] methods for continuum problems. The advantages of the FMM can be huge when large numbers of particles are involved, as it reduces the complexity of calculations from O(N2) to O(N). A more widespread adoption of the FMM algorithm has not occurred, mainly due to the complexity of the algorithm and the considerable programming effort, when compared with other algorithms like particle-mesh methods, or treecodes providing O(N log N) complexity. We are developing an open source implementation of the FMM —with particular application to the calculation of a velocity field induced by N vortex particles. February 2008 -- at this time, we release a stable alfa version of the Python code, and invite interested parties to email us if they would like to collaborate with us on further developments. We distribute this code under the MIT License, giving potential users the greatest freedom possible. We do, however, request fellow scientists that if they use our codes in research, they kindly include us in the acknowledgement of their papers. We do not request gratuitous citations; only cite our articles if you deem it warranted. Related linksPetFMM: Open-sourced Parallel C++ implementation of the Fast Multipole Method. PetFMM can be obtained directly from its repository or from the group's webpage. Publications"Characterization of the errors of the FMM in particle simulations" by Felipe A. Cruz and L. A. Barba. Preprint uploaded to ArXiv on 10 September 2008. Although the literature on the subject provides theoretical error bounds for the FMM approximation, there are not many reports of the measured errors in a suite of computational experiments. We have performed such an experimental investigation, and summarized the results of about 1000 calculations using the FMM algorithm, to characterize the accuracy of the method in relation with the different parameters available to the user. In addition to the more standard diagnostic of the maximum error, we supply illustrations of the spatial distribution of the errors, which offers visual evidence of all the contributing factors to the overall approximation accuracy: multipole expansion, local expansion, hierarchical spatial decomposition (interaction lists, local domain, far domain). This presentation is a contribution to any researcher wishing to incorporate the FMM acceleration to their application code, as it aids in understanding where accuracy is gained or compromised. "Characterization of the accuracy of the fast multipole method in particle simulations" This is the published paper, a revised version of the ArXiV preprint linked above. Published online: May 5, 2009. DOI [Less]

fastmap

最近用Python来处理大量的Log数据，发现Native Python虽然程序简单可靠，但是运行效率上，很成问题，所以计划将一些关键应用部分，用C语言来实现，进一步提高性能。 目前已经实现的两个功能# 对dict的快速序列化和反序列化 ... [More] , key, value只能是String/Int类型，效率是cPickle的600%。(fastmap.dumps, fastmap.loads) #.根据key的hash value对dict进行分区切分操作(fastmap.partition) [Less]

python-proxy

Python Proxy Download v0.1.0 Draft 1