Showing 4 open source projects for "s-transform"

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    JavaParser

    JavaParser

    Java 1-17 Parser and Abstract Syntax Tree for Java

    This project contains a set of libraries implementing a Java 1.0 - Java 17 Parser with advanced analysis functionalities. The project binaries are available in Maven Central. We strongly advise users to adopt Maven, Gradle or another build system for their projects. If you are not familiar with them we suggest taking a look at the maven quickstart projects. Since Version 3.5.10, the JavaParser project includes the JavaSymbolSolver. While JavaParser generates an Abstract Syntax Tree,...
    Downloads: 3 This Week
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  • 2
    litlyx

    litlyx

    Analytics for developers, setup Analytics in 30 seconds

    The easiest, developer-centric analytics tool. Litlyxis an open-source, self-hostable analytics solution for the modern framework. Litlyx offers a unique eyewear cleaning system that includes a special cleaning solution and reusable microfiber swabs. This system is designed to provide a more thorough and eco-friendly way to clean glasses, lenses, and screens. The brand emphasizes sustainability by reducing single-use plastics and promoting long-term use of their products. Their cleaning kit...
    Downloads: 0 This Week
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  • 3
    awesome-single-cell

    awesome-single-cell

    Community-curated list of software packages and data resources

    ...The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. An analytical framework for big-scale single cell data. Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
    Downloads: 0 This Week
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  • 4
    .NET for Apache Spark

    .NET for Apache Spark

    A free, open-source, and cross-platform big data analytics framework

    .NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write...
    Downloads: 0 This Week
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