Open Source Python Information Analysis Software

Modern, privacy-friendly, and detailed web analytics that works without cookies or JS. There are a lot of web analytics tools. Unfortunately, most of them come with the following caveats. They require handing all of your visitors' info to a third-party company They use cookies to track visitors across sessions, so you need to have those annoying cookie notices. They collect so much personal data that even the NSA is jealous. They are closed source and/or expensive, often with limited data portability. They are hard to use. Shynet has none of these caveats. You host it yourself, so the data is yours. It works without cookies, so you don't need any intrusive cookie notices. It collects just enough data to be useful, but not enough to be creepy. It's open source and intended to be self-hosted. And you may even find the interface easy to use.

This project has moved to github at http://lmwtree.devries.ninja.

C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.

GDL - GNU Data Language, a free IDL (Interactive Data Language, see http://ittvis.com/idl/) compatible incremental compiler.

The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

KML is a knowledge base with support of logical modeling. Advanced model is used to represent knowledge as a set of statements similar to natural language sentences. This project hosts a set of model storage library and server (vrb-ols) and clients.

A univariate and multivariate analysis UI. This project is no longer under development. Please use as you wish.

Using this plugin-based framework, you can instantly start working on the *brain* of your bot (irc bot, chatterbot, robot, ...). With support for db, irc, logging and programming-language independent plugins, users can easily enhance the functionality.

BRAHMS is a Modular Execution Framework for dynamical systems. It knits together independently-authored software modules implementing dynamical processes into an integrated system, and supervises the deployment and execution of that system.

Benetech Human Rights Program Utility Library: Collection of utilities used by the Benetech Human Rights programming for processing human rights data in preparation for statistical analysis. Library handles DSV files, name canonicalization, binarray, et

An extensible network forensic analysis framework. Enables rapid development of plugins to support the dissection of network packet captures. This is a major framework update to Dshell. Plugins written for the previous version are not compatible with this version, and vice versa. By extension, dpkt and pypcap have been replaced with Python3-friendly pypacker and pcapy (respectively). Enables development of external plugin packs, allowing the sharing and installation of new, externally-developed plugins without overlapping the core Dshell libraries. Plugins can now use all output modules, available to the command line switch, -O. That does not mean every output module will be useful to every plugin (e.g. using netflow output for a plugin that looks at individual packets), but they are available.

ExpLab is a set of tools that supports the running, documentation and evaluation of computational experiments.

File-Spector is a small, fast and easy to use binary file analyzer and Inspector. It allows the users to format a complete binary file structure and then use it to read any binary file that matches the specified format.

GISBox by Citiviz is a ready-to-use virtual appliance containing an assortment of powerful, free and open source GIS, Data Mining and Analysis, Modelling and Programming software and packages. The pre-configured and tested GISBox allows you to try and use in few minutes a variety of software used by data scientists, engineers, physicists, mathematicians, geomatics engineers, and geographers from around the globe, without installing anything more than once the Oracle VirtualBox™ software (https://www.virtualbox.org/) on your Linux, Windows or Mac computer. The GISBox is free and open, allowing it to be freely distributed, duplicated and passed around, even with your own datasets and in-house applications. If you read this and are related to the Ecole polytechnique de Lausanne - EPFL (http://www.epfl.ch) in Switzerland, please use this link for downloading or support information: http://gisbox.epfl.ch. Happy (geo)Data Hacking! Brought to you by Citiviz' Builders

This project examines techniques to model three-dimensional rigid body motion using the geometric algebra of Dual Quaternions and how such models compare to more traditional models when used in underconstrained filtering applications.

A python package to find repetitive format pattern in HTML pages and extract information from them using this pattern. The idea is that in pages that have some kind of a list, there will be a repetitive pattern for the human eye (the page format).

math lib for .NET. n-dim arrays, complex numbers, linear algebra, FFT, sorting, cells- and logical arrays as well as 3D plotting classes help developing algorithms on every platform supporting .NET. Sources from SVN, binaries: http://ilnumerics.net

This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people.

Ocular is a spreadsheet written entirely in python. Cell contents are evaluated by python after any standard spreadsheet coordinates are parsed. This allows the full Monty from Python to be implemented in a visual environment.

Open Metaheuristic (oMetah) is a library aimed at the conception and the rigourous testing of metaheuristics (i.e. genetic algorithms, simulated annealing, ...). The code design is separated in components : algorithms, problems and a test report generator

Pretty Damn Quick (PDQ) analytically solves queueing network models of computer and manufacturing systems, data networks, etc., written in conventional programming languages. Generic or customized reports of predicted performance measures are output.

Network grep, on steroids

Python module to interface the CLIPS expert system shell library.

PyLogAnalyzer is a tool that receives an input log in black and white, a configuration INI file, which contains the list of rules to process the input, and an output file where to save the results. These rules permit to detect an input line according to a regular expression (regex) or line number range, filter it, pass it, colorise in foreground and background, columnise the groups of the regex and enable or disable the rule. The final goal of this tool is to ameliorate reading long and difficult b/w logs, speed up the analysis and enhance day to day wearying activities. As common application, you can colorise adb logcat logs taking the example files and using them (and also, filter unnecessary lines). You can INSTALL it from PyPI: $ python -m pip install pyloganalyser

Moved to Google Code, see http://code.google.com/p/pythonequations/downloads/list