Open Source Python Information Analysis Software

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.

Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight! When teaching data mining, we like to illustrate rather than only explain.

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.

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"

LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.

*asammdf* is a fast Python parser and editor for ASAM (Associtation for Standardisation of Automation and Measuring Systems) MDF / MF4 (Measurement Data Format) files. It supports MDF versions 2 (.dat), 3 (.mdf) and 4 (.mf4). *asammdf* works on Python 2.7, and Python >= 3.4

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

A lightweight and powerful python module for scientific analysis and visualization of 3d objects. Inspired by the vpython manifesto "3D programming for ordinary mortals", vedo makes it easy to work with 3D pointclouds, meshes and volumes, in just a few lines of code, even for less experienced programmers. vedo is based on VTK and numpy, with no other dependencies. Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud). Export meshes as ASCII or binary to VTK, STL, OBJ, PLY formats. Analysis tools like Moving Least Squares, mesh morphing and more. Tools to visualize and edit meshes (cutting a mesh with another mesh, slicing, normalizing, moving vertex positions, etc..). Split mesh based on surface connectivity. Extract the largest connected area. Calculate areas, volumes, center of mass, average sizes etc. Calculate vertex and face normals, curvatures, feature edges. Fill mesh holes.

The NetEpi project is developing open-source, network-enabled tools for epidemiology and public health practice.

Xplico is a Network Forensic Analysis Tool (NFAT). The goal of Xplico is extract from an internet traffic capture the applications data contained. For example, from a pcap file Xplico extracts each email (POP, IMAP, and SMTP protocols), all HTTP contents, each VoIP call (SIP, MGCP, MEGACO, RTP), IRC, WhatsApp... Xplico is able to classify more than 140 (application) protocols. Xplico cam be used as sniffer-decoder if used in "live mode" or in conjunction with netsniff-ng. Xplico is used also in CapAnalysis: http://www.capanalysis.net

The Chromensics tool is developed to read all information from chrome browser directory and present it to user, in easy readable tabular format which can be explored in descent interface without running the chrome browser. The tool will also allow you retrieve information from other chrome installation brought from different machine for analyzing. The acquired artifacts can be exported in PDF report to present it in court of law or to superiors. -Update 04/02/2017- Standardized all date formats to YYYY-MM-DD HH:MM:SS Introduced Export to Excel Feature Added several search engines results under "Searches" Several Small bug fixes -Update- Now include debug capacity to generate debug logs. If users face any issue with tool, kindly attach cdb.log generated and send it to us. Fixed Cookie and Password Decryption Bug

Python module to interface the CLIPS expert system shell library.

A minimalist approach to share text documents and data annotations. Allows a large number of different annotations to be represented. Project files contain: - simple code to hold/read/write data and perform sample processing. - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We are interested in reuse, and we focus on common NLP tasks that are broadly useful for textmining.

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.

A windrose, also know as polar rose plot, is a special diagram for representing the distribution of meteorological datas, typically wind speeds by class and direction.

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.

Pink is an image processing library developed at ESIEE Engineering for research and teaching purposes. It contains implementations of over 200 algorithms for image segmentation and filtering.

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.

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.

FONZIE is a bioinformatic tool written in Python developed for the genetic cartography speciality. FONZIE allow as well to find markers on a set of sequences than to find associate oligonucleotides. http://www.biomedcentral.com/1756-0500/3/322/abstra

HeWIT helps you create and fill out forms. It tells you if you've missed anything or made any mistakes. You can then send the form over email, upload to a web site, or pass it by memory stick to whoever needs it next.

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

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

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