Open Source Python Data Management Systems - Page 16

RStatServer is a system for quickly developing and deploying web applications that integrate statistical computations and graphics. It is built using the Zope web application develeopment system and the open-source statistical data analysis environment R

The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests.

A tool that parses SQL Select statements and generates a diagram. The diagram shows parts of the underlying SQL directly in the diagram. For example x=30 , GROUP BY (year), HAVING MIN(age) > 18. It is easy to see cartesian joins and/or loops.

Reliability Oriented Bioinformatic Networks Visualization

Rocksnake takes localities from geological mapping projects and plots them in 2D, over an optional basemap. Each locality has all the standard geology notebook contents and photos attached to it which can then be viewed from Rocksnake.

Explore InSAR data and more

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.

SBEVSL is a collaborative project between Dowling and RIT on the development of a Structural Biology Extensible Visualization Scripting Language, so that users can move freely among various molecular graphics tools, such as rasmol, pymol, raster3d, etc.

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

The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.

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.

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.

SciGraphica is a scientific data visualization and analysis application, supplying many of the basic plotting features for 2D and 3D charts. It features opening several worksheets and plots to work with at the same time and PS output.

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

This project delivers Linux support (USB driver and Tools) for a Digital Signal Processor (DSP) cards of the Signal Ranger series, see http://www.softdb.com/product_dsp.shtml for details) connected to a Linux host via USB.

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).