Open Source Data Analytics Tools - Page 2
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The Physics eXtension Library (PXL) is a C++ toolkit for fourvector analysis and hypothesis evolution in high energy physics data analysis.
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R Hadoop for Big Data
Download Free Associated R open source script files for big data analy
Download Free Associated R open source script files for big data analysis with Hadoop and R These are R script source file from Ram Venkat from a past Meetup we did at http://www.meetup.com/R-Matlab-Users/events/85160532/ Also, there is a long video and Powerpoint presentation slide PDF with R files at: http://quantlabs.net/blog/2012/11/how-to-use-hadoop-and-r-for-big-data-parallel-processing-free-download-pdf/ Download source files from http://quantlabs.net/blog/2012/11/download-free-associated-r-open-source-script-files-for-big-data-analysis-with-hadoop-and-r-rstats-hadoop/ -
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Reproducible-research
A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.
In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. -
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A shell for using the methods of Contextual Logic to do qualitative data analysis, mathematical research on the theory underlying Conceptual Knowledge Processing, or learning Formal Concept Analysis. It uses the framework provided by the Tockit project..
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SafeUtils
110+ developer tools as native MacOS, Linux & Windows desktop apps.
Tools: https://safeutils.com/barcode-generator https://safeutils.com/color-picker https://safeutils.com/qr-code-generator https://safeutils.com/qr-code-scanner https://safeutils.com/word-counter https://safeutils.com/base-64-decoder https://safeutils.com/diff-checker https://safeutils.com/hex-to-ascii https://safeutils.com/json-formatter https://safeutils.com/lorem-ipsum-generator https://safeutils.com/random-generator https://safeutils.com/time-converter https://safeutils.com/xml-formatter https://safeutils.com/ascii-to-binary https://safeutils.com/ascii-to-hex https://safeutils.com/base-64-encoder https://safeutils.com/binary-to-ascii https://safeutils.com/case-converter https://safeutils.com/csv-to-json https://safeutils.com/decimal-to-ascii https://safeutils.com/html-formatter https://safeutils.com/html-preview https://safeutils.com/html-to-markdown https://safeutils.com/id-generator https://safeutils.com/json-to-csv https://safeutils.com/json-to-xml -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker. -
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Sorting Algorithms
Searching and Sorting algorithms
Selection Sort, Quick Sort, Merge Sort, etc Data analysis and efficiency. -
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Command line encryption tool for one time, daemon, or stream data processing. Data stats, check sums, conversion to/from text. Data/keys from files, pipes, standard input. In-place/diverted processing or data-analysis-only. Random, file, password keys.
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StreamAlert
StreamAlert is a serverless, realtime data analysis framework
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. -
Build apps or websites quickly on a fully managed platformRun frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure.
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TOPS_CeMM
User Friendly Data Analysis Tool for Interaction Data
TOPS provides the benchtop scientist with a free toolset to analyze, filter and visualize data from functional genomic gene-gene and gene-drug interaction screens with a flexible interface to accommodate various different technologies and analysis algorithms in addition to those already provided here. -
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TrollEditor
Editor of virtual worlds.
Troll enables creation and visualisation of complex environments. It is based on Ogre3D (graphics engine), Bullet (physics engine), PhysX (physics engine), OpenAL (3D sound and music), OpenCV (image analysis and camera model) and Boost (threading and python binding). It uses Python for scripting, as well as built in simple scripting language. Some features: - built in multiplayer - incorporating data from different sensors (IMU, AHRS, Razer Hydra) - image server/client - built in support for motion capture data analysis - path finding using data from physics engine (backwave propagation algorithm) - built in XML parser - graphical world editor - plugins system (C++) -
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VISPA is a novel development environment for high energy physics analyses, based on a combination of graphical and textual steering. The main goal of VISPA is to support users in prototyping, performing, and verifying a data analysis of any complexity.
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Wooey
A Django app that creates automatic web UIs for Python scripts
Wooey is a simple web interface to run command line Python scripts. Think of it as an easy way to get your scripts up on the web for routine data analysis, file processing, or anything else. The project was inspired by how simply and powerfully sandman could expose users to a database and by how Gooey turns ArgumentParser-based command-line scripts into WxWidgets GUIs. Originally two separate projects (Django-based djangui by Chris Mitchell and Flask-based Wooey by Martin Fitzpatrick) it has been merged to combine our efforts. Enable the easy wrapping of any program in simple python instead of having to use language specific to existing tools such as Galaxy. Enable fellow lab members with no command line experience to utilize python scripts. Autodocument workflows for data analysis (simple model saving). -
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ckon
automatic build tool for ROOT data analysis software
ckon is a C++ program/tool which automatically takes care of compilation, dictionary generation and linking of programs and libraries developed for data analyses within the CERN ROOT analysis framework. This includes parsing include headers to figure out which libraries the main programs need to be linked to. It uses automake/autoconf to be platform independent and GNU install compliant. In addition, m4 macros are automatically downloaded and the according compiler flags included based on a list of boost libraries provided in the config file. -
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Interactive Perl environment for data analysis. It consists of several integrated tools such as a Plotting tool, Interactive Shell, and more. Written entirely in Perl using Tk.
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iAIDA
iAIDA is a C++ implementation of the AIDA interfaces.
iAIDA is an implementation in C++ of the AIDA Abstract Interfaces for Data Analysis, a set of interfaces designed for data analysis. Initially designed for use in experimental and theoretical physics, the AIDA interfaces can also be used in other fields. -
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Java OpenCL Process Virtual Machine. Spring IoC based framework for complex data analysis with OpenCL computing.
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pyastrolib is a python library providing professional astronomers a robust set of tools for astronomical data analysis. This project will provide all the functionality as NASA's IDL Astronomy User's Library in addition to other planned features.
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pygolem
Python API for the GOLEM Tokamak discharge database
This simple Python API aims to provide a simple and easy to understand access to the discharge database of the GOLEM Tokamak. The scipy, numpy and matplotlib Python libraries are used for data analysis and plotting. -
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q pipeline manager
q: integrated platform for pipeline configuration and management
The q utility is a platform for creating and managing data analysis pipelines. It expands the value of your existing job scheduler - either Grid Engine or TORQUE PBS - through numerous functions that help you organize, submit, monitor, manage and share your informatics work. Data processing pipelines require high-level organization and parallelization of work to optimize resource utilization and decrease the time to results. q (from queue) allows complex job sequences to be efficiently assembled and managed, including dependency tracking, parallelization, and pipeline-level monitoring, error recovery, and data protection. Pipelines are constructed from modular script files, with job definitions and results stored in easily retrieved job files. A web interface facilitates job submission and monitoring, with the complete pipeline exportable for full transparency.
