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.

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.

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

StreamAlert is a serverless, real-time data analysis framework that empowers you to ingest, analyze, and alert on data from any environment, using data sources and alerting logic you define. Computer security teams use StreamAlert to scan terabytes of log data every day for incident detection and response. Incoming log data will be classified and processed by the rules engine. Alerts are then sent to one or more outputs. Rules are written in Python; they can utilize any Python libraries or functions. Merge similar alerts and automatically promote new rules if they are not too noisy. Ingested logs and generated alerts can be retroactively searched for compliance and research. Serverless design is cheaper, easier to maintain, and scales to terabytes per day. Deployment is automated, simple, safe and repeatable for any AWS account. Secure by design, least-privilege execution, containerized analysis, and encrypted data storage.

*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

We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community, we-media, and online earning community, with a QQ group of more than 10,000 people and at least 10,000 subscribers. The number of Github Stars exceeds 60k, and it ranks in the top 100 of all Github organizations. The daily up of all its websites exceeds 4k, and the peak of Alexa ranking is 20k. Our core members are certified as CSDN blog experts and short-book programmers as excellent authors. We have established ApacheCN, a non-profit document, and tutorial translation project.

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.

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.

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

SPPAS is a scientific computer software package written and maintained by Brigitte Bigi of the Laboratoire Parole et Langage, in Aix-en-Provence, France. Available for free, with open source code, there is simply no other package for linguists to simple use in the automatic annotations of speech, the analyses of any kind of annotated data and the conversion of annotated files. SPPAS is able to produce automatically speech annotations from a recorded speech sound and its orthographic transcription. SPPAS is helpful for the analysis of any annotated data: estimate statistical distributions, make requests, manage files, visualize annotations. SPPAS offers a file converter from/to a wide range of formats: xra, TextGrid, eaf, trs... <https://sppas.org>

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.

Python module to interface the CLIPS expert system shell library.

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

PyPedal is a Python module that provides tools for the manipulation of pedigrees, simple visualization of pedigrees, and the calculation of measures of genetic diversity from pedigrees.

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

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

Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is available both for Unix and Windows platforms (a dedicated platform archive is available on request). Note : if you have downloaded version 3.12 before the 8th of february, a patch exists for a minor bug on TOutputFileKey file, don't hesitate to ask us.

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.

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.

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).

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.

A web-based interface for the Hadoop MapReduce framework that simplifies the process of writing and running MapReduce jobs. Aimed at introducing parallelism concepts in introductory computer science courses.

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.