Open Source Python Data Management Systems - Page 17

GitHub Actions manages automatic synchronization of runs and generation of new pages. Gatsby-generated static pages, fast. Support for Vercel (recommended) and GitHub Pages automated deployment. React Hooks. Mapbox for map display. Supports most sports apps such as nike strava. Automatically backup gpx data for easy backup and uploading to other software.

SWATH-Auto System Analyzer Tool, SASA Tool, is a novel SWATH platform for non-targeted metabolomics data analysis with an accurate mass spectral library for metabolite identification using SWATH acquisition mode.

SCaVis is an environment for scientific computation, data analysis and data visualization for scientists, engineers and students. The program is fully multiplatform (100% Java) and integrated with Java and a number of scripting languages: Jython (Python), Groovy, JRuby, BeanShell. SCaVis can be used to plot functions and data in 2D and 3D, perform statistical tests, data mining, numeric computations, function minimization, linear algebra, solving systems of linear and differential equations. Linear, non-linear and symbolic regression are also available. Elements of symbolic computations using Octave/Matlab scripting are supported. The project was migrated to DataMelt.

The purpose of the SCiDA Pro program is to help with processing solar cell production data. It has the following features: - Able to handle large data sets in a fast way (e.g. plotting 100k cell data takes a few seconds) - Easy data filtering - Easy generation of a data summary report - Extensive data plotting features - Cross-platform (Windows/Linux/MacOS) - Supports multiple languages

SQLBucket is a lightweight framework to help write, orchestrate and validate SQL data pipelines. It gives the possibility to set variables and introduces some control flow using the fantastic Jinja2 library. It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines. To start working, you need to instantiate your SQLBucket core object with the project_folder parameter. That folder will contain all your SQL ETL. The python file where you create your SQLBucket object is also a good place to instantiate your command line interface.

Ad-hoc data replication for SQLServer 2005,2008,2010 and 2012. Touch-and-go design requires you to provide just login info, query file with your SQL and target table name.

Space Time Analysis of Regional Systems: Combines a suite of geocomputational and dynamic visualization modules for the exploratory analysis of data measured for spatial and temporal units.

SVGGraticule let's you create graticules and measured grids used on maps as SVG files.

SZARP is a full-featured, open source (GPL) SCADA system for Linux. Client programs are available for Linux and Windows.

Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. Very often, an entry point needs additional information from the container that is not available in hyperparameters. SageMaker Containers writes this information as environment variables that are available inside the script.

Serve machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed. Containerizing your model and code enables fast and reliable deployment of your model. The SageMaker Inference Toolkit implements a model serving stack and can be easily added to any Docker container, making it deployable to SageMaker. This library's serving stack is built on Multi Model Server, and it can serve your own models or those you trained on SageMaker using machine learning frameworks with native SageMaker support.

A GUI built using Python 2.3+, wxPython and PyOpenGL for the mathematical modeling and visualization of landscape and streamflow systems. This visualization tool uses the Channel-Hillslope Integrated Landscape Development (CHILD) model as its backend.

The Scientific Visualization Artist Tools (SVAT) include converters from the Visualization Toolkit (VTK) to RenderMan Interface Bytestream (RIB) (ASCII and binary files supported), Blender to RenderMan, compilable RenderMan shaders and other utilities.

SenseRank Sys: - builds the dictionaries (multidim matrices) of words’ values; - for the set utterance in certain language builds a figure in multidimensional space (in the matrix space) of values (visual schema), which is topological view of sense

C++ and Python based open source graph visualization and analysis software, primarily intended for use in the analysis of social networks (other applications are also conceivable).

SloppyPlot is a tool to import scientific datasets and create 2D plots from this data. There is a gnuplot postscript export available.

SnapLogic is an Open Source Data Integration framework that combines the power of state-of-the-art dynamic programming languages with standard Web interfaces to solve today's most pressing problems in data integration.

Soto is a graphical message debugger for simpleIPC. In addition to displaying the message traffic between processes, it can log and play back this traffic. Soto also provides the ability to design GUI "panes" that are populated with data from messages.

Now maintained at github.com/spacepy/spacepy Space Science library for Python - contains superposed epoch classes, drift shell tracing, access to magnetic field models, streamline tracing, bootstrap confidence limits, time and coordinate conversions, etc.

Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more.

Spire is a print stream converter/manipulator. It can transform print streams from Metacode to Postscript, Postscript to Metacode. PCL support will be added soon. Spire is also capable of sorting documents (think postal sortation) and added barcodes.

The Stats Jam project is an extension to Mediawiki that allows users to embed database queries and visualisations into their wiki pages.

StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can contain people as nodes and friendships between them as links, with data like a person’s age and the date a friendship was established. StellarGraph supports the analysis of many kinds of graphs. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn.

StormForce is a free open source program written in Python/PyGame for the Boltek LD-250/StormTracker. StormForce works in FreeBSD, Linux, and Microsoft Windows.

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.