Projects tagged ‘matplotlib’

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

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 ... [More] processing algorithms. The software was developed within the Visualisation group. DeVIDE's primary (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]

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 ... [More] scripter for Tremulous gamers as well. The current version can: generate graphs of kills and deaths 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]

This project creates various types of statistics and graphs from subversion repository log data.New Version 0.5.12 Available (12 Dec 2009) - commit trend historgram is added. Also --maxdircount which ... [More] limits the number of directories on the graph to the num largest directories (Thanks kitpz2). New Version 0.5.11 Available (18 Oct 2009) with a bug fix for unicode filenames. 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]

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

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 ... [More] to test and compare your algorithms. It's the Swiss army knife for machine learning and neural networking. [Less]

SIP/TDIP Dipole-Dipole Designer TDEM Designer

Python class to edit the different components of a Matplotlib figures. It can edit/copy/delete "figure", "axes", "lines" and "image" components.

A simple python middleware layer for dynamic visualizations of relational data. The plot factory provides an agile framework for designing and publishing new products. Developers author product ... [More] specifications in XML and upload them to the product factory server for reuse in mashups and other lightweight applications. Original goals: Goal 1: Provide user-configurable, real-time web visualizations for data stored in a relational databases. How: Replace static, pre-generated products with products generated directly from query results. Goal 2: Simplify the development of "one-off" web applications. How: Provide a web service for visualizations so that images can be easily embedded in other apps. Goal 3: Significantly reduce the amount of code required to create a new configurable visualization. How: Distill each visualization to 1) a set of parameters, 2) a SQL statement, and 3) a plotting script. Ensure that all other code is written only once and is 100% generic. Goal 4: Significantly reduce the amount of code required to keep web visualizations in sync with incoming data. How: Dynamically-generated products don't need to be kept in sync. (Some slow queries may require caching and pre-generation, features included in the factory) Goal 5: Allow access to the underlying data driving a visualization. How: Ensure that every visualization automatically supports access to its source data. Aliases: the Product Factory; Pyrview [Less]

This project is motivated from the research purpose plot. Everyday we generate millions of data and we want to analyze it and draw beautiful graphs to present it. But there is not a standard tool to do that. That's why this project comes.