Search Results for "synthetic"
Sort By:
Showing 148 open source projects for "synthetic"
View related business solutions-
Define and Deliver Comprehensive Cybersecurity Services. Security threats continue to grow, and your clients are most likely at risk. Small- to medium-sized businesses (SMBs) are targeted by 64% of all cyberattacks, and 62% of them admit lacking in-house expertise to deal with security issues. Now technology solution providers (TSPs) are a prime target. Enter ConnectWise Cybersecurity Management (formerly ConnectWise Fortify) — the advanced cybersecurity solution you need to deliver the managed detection and response protection your clients require. Whether you’re talking to prospects or clients, we provide you with the right insights and data to support your cybersecurity conversation. From client-facing reports to technical guidance, we reduce the noise by guiding you through what’s really needed to demonstrate the value of enhanced strategy.
-
Managing your therapy and rehab practice is a time-consuming process. You spend hours on paperwork, billing, scheduling, and more. Raintree’s Therapy & Rehab EHR is here to help you manage your practice more efficiently. With our all-in-one solution, you’ll get the tools you need to streamline your therapy and rehab practice, improve patient care, and get back to doing what you love.
-
1
YData Synthetic
Synthetic data generators for tabular and time-series data
A package to generate synthetic tabular and time-series data leveraging state-of-the-art generative models. Synthetic data is artificially generated data that is not collected from real-world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series... -
2
Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent... -
3
Intel RealSense
Intel® RealSense SDK
Intel® RealSense™ SDK 2.0 is a cross-platform library for Intel® RealSense™ depth cameras. The SDK allows depth and color streaming and provides intrinsic and extrinsic calibration information. The library also offers synthetic streams (point cloud, depth aligned to color and vise-versa), and built-in support for recording and playback of streaming sessions. Intel has EOLed the LiDAR, Facial Authentication, and Tracking product lines. These products have been discontinued and will no longer... -
4
Synthea Patient Generator
Synthetic Patient Population Simulator
SyntheaTM is an open-source, synthetic patient generator that models the medical history of synthetic patients. Our mission is to provide high-quality, synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions, enabling research with Health IT data that is otherwise legally or practically unavailable. The models used to generate synthetic patients are informed... -
We design our technology to make workforce management easier. APS offers core HR, payroll, benefits administration, attendance, recruiting, employee onboarding, and more.
-
5
Copulas
A library to model multivariate data using copulas
Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table of numerical data, use Copulas to learn the distribution and generate new synthetic data following the same statistical properties. Choose from a variety of univariate distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas. Compare real and synthetic data visually after building your model. Visualizations are available as 1D... -
6
SDGym
Benchmarking synthetic data generation methods
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... -
7
Cryptocurrency Exchange Feed Handler
Cryptocurrency Exchange Websocket data feed handler
... for you. Start the feed handler and you're done! Cryptofeed also provides a synthetic NBBO (National Best Bid/Offer) feed that aggregates the best bids and asks from the user specified feeds. Cryptofeed supports backend callbacks that will write directly to storage or other interfaces. Cryptofeed can be installed from PyPi. (It's recommended that you install in a virtual environment of your choosing). -
8
CTGAN
Conditional GAN for generating synthetic tabular data
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity. If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. When using the CTGAN library directly, you may... -
9
NetworkX
Network analysis in Python
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Data structures for graphs, digraphs, and multigraphs. Many standard graph algorithms. Network structure and analysis measures. Generators for classic graphs, random graphs, and synthetic networks. Nodes can be "anything" (e.g., text, images, XML records). Edges can hold arbitrary data (e.g., weights, time-series). Open source 3-clause BSD license. Well tested... -
Exact Payments delivers easy-to-integrate embedded payment solutions enabling you to rapidly onboard merchants, instantly activate a variety of payment methods and accelerate your revenue — delivering an end-to-end payment processing platform for SaaS businesses.
-
10
Gretel Synthetics
Synthetic data generators for structured and unstructured text
Unlock unlimited possibilities with synthetic data. Share, create, and augment data with cutting-edge generative AI. Generate unlimited data in minutes with synthetic data delivered as-a-service. Synthesize data that are as good or better than your original dataset, and maintain relationships and statistical insights. Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports... -
11
Twinify
Privacy-preserving generation of a synthetic twin to a data set
twinify is a software package for the privacy-preserving generation of a synthetic twin to a given sensitive tabular data set. On a high level, twinify follows the differentially private data-sharing process introduced by Jälkö et al.. Depending on the nature of your data, twinify implements either the NAPSU-MQ approach described by Räisä et al. or finds an approximate parameter posterior for any probabilistic model you formulated using differentially private variational inference (DPVI... -
12
Synthetix
Synthetix Solidity smart contracts
Synthetix is a new financial primitive enabling the creation of synthetic assets, offering unique derivatives and exposure to real-world assets on the blockchain. Leveraging the Synthetix debt pool and our innovative liquidity framework, our traders are guaranteed to have some of the best price execution around, with little to no slippage and fills you can’t get elsewhere. Capture the price movements of popular cryptocurrencies, fiat currencies, stocks, commodities and more with zero slippage... -
13
Inferno
React-like JavaScript library for building modern user interfaces
Inferno is an insanely fast, React-like library for building high-performance user interfaces on both the client and server. React-like API, concepts and component lifecycle events. Switch over easily with inferno-compat. One of the fastest front-end frameworks for rendering UI in the DOM, making 60 FPS on mobile possible. Isomorphic rendering on both client and server, along with fast-booting from server-side renders. Inferno doesn't have a fully synthetic event system like React does. Inferno... -
14
BlenderProc
Blender pipeline for photorealistic training image generation
A procedural Blender pipeline for photorealistic training image generation. BlenderProc has to be run inside the blender python environment, as only there we can access the blender API. Therefore, instead of running your script with the usual python interpreter, the command line interface of BlenderProc has to be used. In general, one run of your script first loads or constructs a 3D scene, then sets some camera poses inside this scene and renders different types of images (RGB, distance,... -
15
Synth
The Declarative Data Generator
Synth is an open-source data-as-code tool that provides a simple CLI workflow for generating consistent data in a scalable way. Use Synth to generate correct, anonymized data that looks and quacks like production. Generate test data fixtures for your development, testing, and continuous integration. Generate data that tells the story you want to tell. Specify constraints, relations, and all your semantics. Seed development and environments and CI. Anonymize sensitive production data. Create... -
16
Bogus
A simple and sane fake data generator for C#, F#, and VB.NET
Bogus is a simple and sane fake data generator for .NET languages like C#, F# and VB.NET. Bogus is fundamentally a C# port of faker.js and inspired by FluentValidation's syntax sugar. Bogus will help you load databases, UI and apps with fake data for your testing needs. When Bogus updates locales from faker.js or issues bug fixes, sometimes deterministic sequences can change. Changes to deterministic outputs are usually highlighted in the release notes. Changes to deterministic outputs is... -
17
Mimesis
High-performance fake data generator for Python
Mimesis is an open source high-performance fake data generator for Python, able to provide data for various purposes in various languages. It's currently the fastest fake data generator for Python, and supports many different data providers that can produce data related to people, food, transportation, internet and many more. Mimesis is really easy to use, with everything you need just an import away. Simply import an object, called a Provider, which represents the type of data you... -
18
Chronos Forecasting
Pretrained (Language) Models for Probabilistic Time Series Forecasting
... as synthetic data generated using Gaussian processes. -
19
Datadog Client Libraries for Go
Datadog Go Library including APM tracing, profiling, and security
Datadog Application Performance Monitoring (APM) gives deep visibility into your applications with out-of-the-box performance dashboards for web services, queues, and databases to monitor requests, errors, and latency. Distributed traces seamlessly correlate to browser sessions, logs, profiles, synthetic checks, network, processes, and infrastructure metrics across hosts, containers, proxies, and serverless functions. Navigate directly from investigating a slow trace to identifying the specific... -
20
nlpaug
Data augmentation for NLP
This Python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenters together. -
21
Kuberhealthy
A Kubernetes operator for running synthetic checks as pods
Kuberhealthy is a Kubernetes operator for synthetic monitoring and continuous process verification. Write your own tests in any language and Kuberhealthy will run them for you. Automatically creates metrics for Prometheus. Includes simple JSON status page. Kuberhealthy lets you continuously verify that your applications and Kubernetes clusters are working as expected. By creating a custom resource (a KuberhealthyCheck) in your cluster, you can easily enable various synthetic tests and get... -
22
This program will convert just two channels to full 7.1 covered surround scene. Real psychoacoustics on the fly. Absolutely new technology by phase interference and partial time shifting. Source channels dividing to fifty virtual channels to make their magic and combine to 7.1 You have to use "Equalizer APO" as engine. Please install it before continuing. https://sourceforge.net/projects/equalizerapo/
-
23
Tellurium
Model, simulate, and analyze biochemical systems using one tool.
Tellurium (te.) is a Python environment supporting Spyder2 IDE and Jupyter Notebook aimed for large-scale systems and synthetic biology simulation. It combines a number of existing libraries, including libSBML, libRoadRunner (including libStruct), libAntimony, and is extensible via tePlugins. In addition other tools kits such as matplotlib and NumPy are used to provide additional analysis and plotting support. -
24
TSNE-CUDA
GPU Accelerated t-SNE for CUDA with Python bindings
... taken compared to other state of the art algorithms on synthetic datasets with 50 dimensions and four clusters for varying numbers of points. Note the log scale on both the points and time axis, and that the scale of the x-axis is in thousands of points (thus, the values on the x-axis range from 1K to 10M points. Dashed lines on SkLearn, BH-TSNE, and MULTICORE-4 represent projected times. Projected scaling assumes an O(nlog(n)) implementation. -
25
DBFeeder
Highly Customizable Test Data Generator
DBFeeder is a great tool to generate synthetic testdata for Oracle Databases and it is ideal for companies who wants to outsource development. Thanks to his original approach, data can be highly customizable and it even fits primary and foreign keys constraints of tables.