Open Source Python Information Analysis Software for BSD

SciDAVis is a user-friendly data analysis and visualization program primarily aimed at high-quality plotting of scientific data. It strives to combine an intuitive, easy-to-use graphical user interface with powerful features such as Python scriptability.

IRAMUTEQ : Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires. Logiciel de traitement de données pour des corpus texte ou de type individus/caractères. Permet notamment de réaliser des analyses de type "ALCESTE"

Grafana OSS is the leading open-source platform for visualization and observability. It enables teams to query, visualize, alert on, and explore telemetry data from multiple sources in a single interface. With support for 100+ data source plugins—including Prometheus, Loki, Elasticsearch, InfluxDB, SQL/NoSQL databases, and OpenTelemetry—Grafana helps teams correlate metrics, logs, and traces across applications and infrastructure. Users can build interactive dashboards with rich visualizations, template variables, and reusable panels to monitor systems and troubleshoot issues in real time. Grafana includes capabilities such as ad hoc data exploration, alerting, annotations, and flexible query support. Its extensible plugin ecosystem integrates with cloud platforms, databases, and developer tools—allowing teams to build observability workflows without vendor lock-in. The easiest way to get started with Grafana is with Grafana Cloud, our fully managed, full-stack observability platform.

QtiPlot is a user-friendly, platform independent data analysis and visualization application similar to the non-free Windows program Origin.

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

Part-of-speech tagging is the task of assigning symbols from a particular set to words in a natural language text. ACOPOST implements and extends well-known machine learning techniques and provides a uniform environment for testing.

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

Diversity assessments and comparisons of large compound databases require calculating similarities of millions of compounds in an affordable time. ParaSim addresses this challenge by parallelizing the calculations according to the number of computing cores available on a single machine. It is optimized for the throughput of very large numbers of query structures against very large numbers of reference structures. As as special feature, ParaSim allows to store and and to access frequently queried datasets as persistant objects in memory for short response times. ParaSim calculates chemical similarities based on binary structural fingerprints. It does not compute fingerprints by itself but relies on third party software to do so. Basically, all types of structural fingerprints which can be stored in an array of bits (a bitset) can be used by ParaSim. See the Wiki (https://sourceforge.net/p/parasim/wiki/Documentation/) for detailed documentation. FEEDBACK HIGHLY APPRECIATED!

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.

Dowser is a research tool for the web. It clusters results from search engines, associates words that appear in previous searches, and keeps a local cache of all the results you click on in a searchable database. It helps you keep track of what you find.

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 cross-platform text analysis program written in Python and Free Pascal/Lazarus which scans a whole text file (in plain text, HTML, EPUB, or ODT formats) and ranks all used words according to frequency, performing a quantitative analysis of the text using Shannon-Weaver information statistic and Zipf power law function. It counts words, sentences, chars, spaces, and syllables. Also computes readability indexes (Gunning-Fog, Coleman-Liau, Automated Readability Index (ARI), SMOG grade, Flesch–Kincaid grade level and Flesch Reading Ease).

Augustus development has moved to google code. The new project page is augustus.googlecode.com. New releases of the project are not currently being released to sourceforge. Augustus is designed for statistical and data mining models and produces and consumes models with 10,000s of segments. Versions of Augustus support PMML 3, 4.0.1, and 4.1.

Autoplot is an interactive browser for data on the web. Give Autoplot a URL or local file name and it creates a sensible plot of the data. Autoplot allows you to interactively browse data stored in ascii, .cdf, netcdf, and many other formats. Autoplot's source has been moved to GitHub. Thanks to SourceForge for many years of hosting!

Buildes is an integrated development environment that assists the user in creating building information. It reads a text (session) file from which it compiles the information. It then allows the user to browse, analyze and export the resulting building knowledge. The compilation system is written in pure Python. The building components created are rendered in PythonOCC. The GUI is written using PyQt.

RISO: distributed, heterogeneous Bayesian belief networks. Belief network: a probability model defined on an acyclic directed graph; distributed: nodes can be on different hosts; and heterogeneous: allowing different types of conditional distributions.

This is a implementation of the 'enhanced Topic-based Vector Space Model' (eTVSM) using the python language. A Java-Version and maybe other java-code contributions are planned.

This software presents a flexible and configurable proposal for monitoring and management of real and virtual HPC infrastructures, compatible with paradigm of cloud computing. We help you to answer: 1) What is the performance of my resources? 2) What equipment and resources do we have already? 3) What do we need to upgrade or repair? 4) What can we consolidate to reduce complexity or reduce energy use? 5) What resources would be better reused somewhere else? Status: PreAlpha, so any help shall be welcome. Made for LINUX. GNU General Public License version 3.0 (GPLv3)

IMPORTANT: The project moved over to github! You can find it at: https://github.com/exhuma/python-cluster

A.I. security app. Development ceased.

This is a prototype implementation of "A Network of Ideas" (ANOI) content system.

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.

This is dedicated to development of a small class for organizing data in both a traditional parent<->child hierarchy and by the functions used to generate child data nodes from parent data. The core of the project is in the class definitions contained within arachne.py, a demonstration of how this method of arranging data can be used to solve a relatively interesting problem may be found in test.py

The aim of this project is to translate the geostatistical BMELib Matlab Toolbox (http://www.unc.edu/depts/case/BMELIB/) into Python.

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.