Open Source Mac JSON Software - Page 12
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CyberOPC Sample Client(Windows Forms) included with a CyberOPC Client Library. Based on the CyberOPC Specification (www.cyberopc.org)
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DAW JSON Link v3
Fast, convenient JSON serialization and parsing in C++
Fast, convenient JSON serialization and parsing in C++. The DAW JSON Link library is a high-performance, no allocation, C++ JSON Library. Direct parsing of JSON to your own data structures via simple declarative mappings. Earlier error reporting of unexpected data and errors. Low memory requirement, parser requires a small amount of stack but does not allocate itself. Optimizations based on data types. Does not require owning the mapped types and is not intrusive. -
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DSL-JSON library
High performance JVM JSON library
Fastest JVM (Java/Android/Scala/Kotlin) JSON library with advanced compile-time data binding support. Java JSON library designed for performance. Originally built for invasive software composition with DSL Platform compiler. v1 core library was targeting Java6, while v2 targets Java8. So there is no need anymore for dsl-json-java8 project and instead, just dsl-json project should be referenced. From v2 only a single annotation processor is supported, which was previously in the java8 project. Joda and some other specific libraries were removed. If you still need converters for those types, you need to include them in your project. -
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DataSet Serialize for Delphi and Lazarus
JSON to DataSet and DataSet to JSON converter for Delphi and Lazarus
DataSet Serialize is a set of features to make working with JSON and DataSet simple. It has features such as exporting or importing records into a DataSet, validate if JSON has all required attributes (previously entered in the DataSet), exporting or importing the structure of DataSet fields in JSON format. In addition to managing nested JSON through master detail or using TDataSetField (you choose the way that suits you best). All this using class helpers, which makes it even simpler and easier to use. -
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DefiantJS
Provides the ability for you to build smart templates applications
Do you need to query large JSON structures? Do you end up coding loops to parse the JSON and identify the data that matches your query? Defiant offers a better way. Defiant extends the global JSON object with a "search" method that enables lightning-fast searches using XPath expressions. Try out the XPath Evaluator to get the idea. DefiantJS provides the ability for you to build smart templates applicable on JSON structures, based upon proven & standardized technologies such as XSLT and XPath. As of v2.0.0, Defiant can be utilized in the Node.js environment. Since there is no default support for XML technologies in Node, the library puppeteer is initiated to mimic the browser environment, with Defiant running. All methods called from Node is piped via Puppeteer. Even though, Defiant now can be used in the Node environment - utilizing Defiant in the browser is recommended. It all boils down to whether you want your servers to run thousands of searches for your clients. -
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Directory Structure To Json
Folder Structure >>> JSON File
Just select any folder from your computer and save equivalent JSON file. -
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Django REST Pandas
Serves up Pandas dataframes via the Django REST Framework
Django REST Pandas (DRP) provides a simple way to generate and serve pandas DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. This capability can often be leveraged by sending users to the same URL that your visualization code uses internally to load the data. While DRP is primarily a data API, it also provides a default collection of interactive visualizations through the @wq/chart library, and a @wq/pandas loader to facilitate custom JavaScript charts that work well with CSV output served by DRP. These can be used to create interactive time series, scatter, and box plot charts. -
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DocArray
The data structure for multimodal data
DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API. Door to multimodal world: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases. -
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Document Object Model Structured Query Language! Example: 'SELECT {//title[1]=>innerCode} FROM {http://example.com}';
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EVReflection
Reflection based (Dictionary, CKRecord, NSManagedObject, Realm, JSON
At this moment the master branch is tested with Swift 4.2 and 5.0 beta If you want to continue using EVReflection in an older version, then use the corresponding branch. Run the unit tests to see EVReflection in action. In most cases, EVReflection is very easy to use. Just take a look at the section It's easy to use. But if you do want to do nonstandard specific things, then EVReflection will offer you an extensive range of functionality. -
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Extended Memory Semantics (EMS)
Persistent shared object memory and parallelism for Node.js and Python
EMS makes possible persistent shared memory parallelism between Node.js, Python, and C/C++. Extended Memory Semantics (EMS) unifies synchronization and storage primitives to address several challenges of parallel programming. A modern multi-core server has 16-32 cores and nearly 1TB of memory, equivalent to an entire rack of systems from a few years ago. As a consequence, jobs formerly requiring a Map-Reduce cluster can now be performed entirely in shared memory on a single server without using distributed programming. -
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FArP is JavaScript function arguments processor. It provides an easy way to parse function input arguments against expected types and values. Thanks to it developers can avoid chains of if conditions at the beginnings of their functions.
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FeatureFlags
Allows developers to configure feature flags
FeatureFlags makes it easy to configure feature flags, A/B and MVT tests via a JSON file which may be bundled with your app or hosted remotely. For remotely-hosted configuration files, you may enable/disable features without another release to the App Store, update the percentages of users in A/B test groups or even roll out a feature previously under A/B test to 100% of your users once you have decided that the feature is ready for prime time. -
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FileMasta
A search application to explore, discover and share online files
A search application to explore, discover, and share online files. A federated search application for discovering interesting files that are being shared online, such as video, music, books, software, games, subtitles and much more. All data is crawled by od-database which collects information about the contents of servers worldwide. We don’t search file contents. We host no content, we provide only access to already available files in the same way Google and other search engines do. -
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Flutter Login Example
100% Shared Code Android/iOS Login Example
This example uses a ScrollView, JSON Rest API, Navigation, Alert Pop-Up, Progress Indicator, Globals, Images in a shared asset folder, and 100% Shared Code. Now with the ability to login with FaceID, TouchID, and Fingerprint Reader on Android. -
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FormSchema Native
Generate a form using JSON Schema and Vue.js
Vue component form based on JSON Schema and Native HTML. -
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Framework Benchmarks
Source for the TechEmpower Framework Benchmarks project
If you're new to the project, welcome! Please feel free to ask questions here. We encourage new frameworks and contributors to ask questions. We're here to help! This project provides representative performance measures across a wide field of web application frameworks. With much help from the community, coverage is quite broad and we are happy to broaden it further with contributions. The project presently includes frameworks on many languages including Go, Python, Java, Ruby, PHP, C#, F#,Clojure, Groovy, Dart, JavaScript, Erlang, Haskell, Scala, Perl, Lua, C, and others. The current tests exercise plaintext responses, JSON serialization, database reads and writes via the object-relational mapper (ORM), collections, sorting, server-side templates, and XSS counter-measures. Future tests will exercise other components and greater computation. -
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This is the most advanced and complete new generation tool for creating Web applications. You can create your own "Web Applications" once writing very few lines of code and make it simply accessible from anywhere in the world.
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GCDWebServer
The #1 HTTP server for iOS, macOS & tvOS
GCDWebServer is a modern and lightweight GCD based HTTP 1.1 server designed to be embedded in iOS, macOS & tvOS apps. Elegant and easy to use architecture with only 4 core classes: server, connection, request and response. Well designed API with fully documented headers for easy integration and customization. Entirely built with an event-driven design using Grand Central Dispatch for best performance and concurrency. No dependencies on third-party source code. Parser for web forms submitted using "application/x-www-form-urlencoded" or "multipart/form-data" encodings (including file uploads). Allow implementation of fully asynchronous handlers of incoming HTTP requests. Minimize memory usage with disk streaming of large HTTP request or response bodies. Automatically handle transitions between foreground, background and suspended modes in iOS apps. Full support for both IPv4 and IPv6. -
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Glue Stick dependency injection framework is used for assembling and configuring Java applications from external bean definition and configuration files. There are several supported bean definition file formats, such as Groovy, JSON and Spring XML.
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Go JSON Diff
Go JSON Diff
Go JSON Diff. -
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Go support for Protocol Buffers
The Go support for Google's protocol buffers
Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data, think XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. Protocol buffers currently support generated code in Java, Python, Objective-C, and C++. With our new proto3 language version, you can also work with Dart, Go, Ruby, and C#, with more languages to come. Package proto provides functions operating on protobuf messages such as cloning, merging, and checking equality, as well as binary serialization and text serialization. Package jsonpb serializes protobuf messages as JSON. Package ptypes provides helper functionality for protobuf well-known types. -
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GoJSONQ
A simple Go package to Query over JSON/YAML/XML/CSV Data
A simple Go package to Query over JSON Data. It provides a simple, elegant, and fast ODM-like API to access, and query JSON documents. You can query your document using the various query methods such as Find, First, Nth, Pluck, Where, OrWhere, WhereIn, WhereStartsWith, WhereEndsWith, WhereContains, Sort, GroupBy, SortBy, and so on. Also, you can aggregate data after query using Avg, Count, Max, Min, Sum, etc. -
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Groot
From JSON to Core Data and back
Groot provides a simple way of serializing Core Data object graphs from or into JSON. It uses annotations in the Core Data model to perform the serialization. -
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HTTP functional and non-functional (load and performance) toolkit based on jython/grinder (http://grinder.sf.net) ...includes capabilities to support: SOA services, REST, json/xml encoding, AES and WS security ... and a stub to collect requests
