Projects

SHOGUN

The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It comes with a generic interface for SVMs, features several SVM and kernel implementations, includes LinAdd optimizations and also Multiple Kernel Learning algorithms. ... [More] SHOGUN also implements a number of linear methods. It allows the input feature-objects to be dense, sparse or strings and of type int/short/double/char. It provides efficient implementations several kernels but also linear methods, hidden markov models etc. and interfaces to matlab,octave,python,R and has a cmdline interface and allows C++ extensions via a library. [Less]

iTrade

Trading & Charting system written in Python including Quotes Management, Historic Data, Live Data, Import/Export, Charting, candlestick and Technical analysis, automated alerts, portfolio management, risk management, and much much more

PyBrain

PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. It's the Swiss army knife for machine learning and neural networking.

svnplot

This project creates various types of statistics and graphs from subversion repository log data.News/UpdatesNew version 0.6 available (28 Mar 2010) Many bug fixes related to line count computation when folders are deleted or renamed. (Thanks to Cyber Sinh for detailed bug reports and testing). New ... [More] svnplot-js.py for JavaScript canvas based charts using jqPlot library. New Version 0.5.14 Available (4 Feb 2010) - detection of binary files based on list of commonly used binary files extension. Improvements in calculating the diffs for large repositories where you can access repository as 'file://' repository. DO NOT USE 0.5.13. Version 0.5.13 has a bug in the linecount computations. If you are using 0.5.13, please discard the repository stats database and regenerate it again. Steps to generate these statistics : subversion log information is first converted into a sqlite database. then using sql queries various stats are generated these stats are converted into graphs using the matplotlib package The various graphs generated are inspired by the graphs generated using StatSVN/StatCVS. Currently following statistics and graphs are generated General Statistics Revision count Author count File Count Head revision number Top 10 Hot List Top 10 Active Authors Top 10 Active Files LoC graphs total loc line graph (loc vs dates) average file size vs date line graph Contributed lines of code line graph (loc vs dates). Using different colour line for each developer Loc and Churn graph (loc vs date, churn vs date)- Churn is number of lines touched (i.e. lines added + lines deleted + lines modified) File Count graphs file count vs dates line graph file type vs number of files horizontal bar chart Directory size graphs directory size vs date line graph. Using different coloured lines for each directory directory size pie chart (latest status) Directory file count pie char(latest status) Commit Activity Graphs Commit Activity Index Activity by hour of day bar graph (commits vs hour of day) Activity by day of week bar graph (commits vs day of week) NEW Author Commit trend history (histogram of time between consecutive commits by same author) Author Activity horizontal bar graph (author vs adding+commiting percentage) Commit activity for each developer - scatter plot (hour of day vs date) Others Tag cloud of words from revision log messages. Tag cloud of author names. These scripts depend on following python packages pysvn - Python interface to subversion sqlite3 - Included by default in python distribution matplotlib - python graph library Currently I am experimenting with applying social network analysis to repositories. Check the preliminary results at Social Network Analysis of Rietveld Subversion Repository and Treemap of Commit count vs centrality for Rietveld repository I am a novice to python, sqlite and matplotlib. So any suggestions on improvements are welcome. [Less]

DeVIDE

DeVIDE, or the Delft Visualisation and Image processing Development Environment, is a cross-platform software framework for the rapid prototyping, testing and deployment of visualisation and image processing algorithms. The software was developed within the Visualisation group. DeVIDE's primary ... [More] (and currently only) front-end is a data-flow boxes-and-lines network editor. In this regard, it is very similar to AVS, OpenDX, Khoros or VISSION. DeVIDE integrates functionality from libraries such as VTK, ITK, GDCM, DCMTK, numpy and matplotlib. It is being very actively developed. [Less]

pyspread

Pyspread is a non-traditional spreadsheet application that is based on and written in the programming language Python. The goal of pyspread is to be the most pythonic spreadsheet. Pyspread expects Python expressions in its grid cells, which makes a spreadsheet specific language obsolete. Each ... [More] cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Pyspread is designed for Linux and other GTK platforms. [Less]

tremcs

Tremulous Clientside Statistics, or TremCS for short, is a statistics script that was written in Python originally to generate useful stats for Tremulous but has now evolved into an event based scripter for Tremulous gamers as well. The current version can: generate graphs of kills and deaths ... [More] during a game. generate graphs over a long period of time to see how you are improving...if at all. create themable banners, avatars and even animated userbars! create web pages. output text stats. upload all of the above to a website via FTP or view it locally. It has been designed to be multiplatform and works on Linux, Windows and MacOS (but has only been tested on Linux) This project is being prepared for release. [Less]

dicompyler

dicompyler is an extensible, fully open source radiation therapy research platform based on the DICOM standard. It is written in Python and is built on pydicom, wxPython, and matplotlib and runs on Windows, Mac OS X and Linux.

PacStats

PacStats is able to analyze the log and the lib directory of the ArchLinux package manager (pacman) and generate statistical charts. The GUI is programmed in Python with PyGTK, the internal database is based on SQLite and the charts are made with Matplotlib.

Powerdroid

Python libraries to interface to GPIB/IEEE-488 instruments, as well as others. Control, fetch measurements, and report. Uses unit objects.