Download JAR files tagged by jython with all dependencies

Jython is an implementation of the high-level, dynamic, object-oriented language Python written in 100% Pure Java, and seamlessly integrated with the Java platform. It thus allows you to run Python on any Java platform.

This plugin makes use of Jython and 2 Pthyon modules (Boto and Troposphere) to allow for automated generation of AWS CloudFormation templates from a Python source file. This alleviates the need to maintain a large and possibly cumbersome JSON file by hand, and allows for proper commenting, modularization, etc.

The Grinder load testing framework. The Grinder is a load testing framework that makes it easy to run a distributed test using many load injector machines. Test scripts are written in Jython or Clojure. HTTP scripts can be recorded easily from a browser session.

The Grinder load testing framework. The Grinder is a load testing framework that makes it easy to run a distributed test using many load injector machines. Test scripts are written in Jython or Clojure. HTTP scripts can be recorded easily from a browser session.

Wrapper classifiers for Jython and Groovy code. Even though the classifier is serializable, the trained classifier cannot be stored persistently. I.e., one cannot store a model file and re-load it at a later point in time again to make predictions.

dyna4JDBC is a JDBC driver, that allows running alternative JVM language scripts (Groovy, JavaScript, Scala, Jython, Clojure, BeanShell, R (Renjin), JRuby etc.) or external console-oriented programs via the JDBC API. Mainly targeted at allowing JDBC-enabled business intelligence applications to run various scripts instead of SQL easily, dyna4JDBC captures and parses the output of scripts/external programs and presents that as a standard JDBC Result Set, allowing the output to be processed further for various purposes like building complex reports quickly and analysis with other tools.

The Java Algebra System (JAS) is an object oriented, type safe and multi-threaded approach to computer algebra. JAS provides a well designed software library using generic types for algebraic computations implemented in the Java programming language using the JVM runtime infrastructure. The library can be used as any other Java software package or it can be used interactively or interpreted through a jython (Java Python) or jruby (Java Ruby) front end, there is also an Android App based on Ruboto (jruby for Android). The focus of JAS is at the moment on commutative, solvable and non-commutative polynomials, power series, Groebner bases, factorization, real and complex roots and applications. By the use of Java as implementation language JAS is 64-bit and multi-core CPU ready and can make use of mutiple CPUs where available. JAS can run on a wide variety of devices ranging from Android to compute clusters (using MPJ a Java Message Passing Interface (MPI) or OpenMPI).

The IntelligentGraph SAIL offers an extended capability for embedded calculation support within any RDF graph. When enabled as an RDF4J SAIL, it offers calculation functionality as part of the RDF4J engine, on top of any RDF4J repository, using a variety of script engines including JavaScript, Jython, and Groovy. It preserves the SPARQL capability of RDF4J, but with additional capabilities for calculation debugging and tracing. IntelligentGraph includes the PathQL query language. Just as a spreadsheet cell calculation needs to access other cells, an IntelligentGraph calculation needs to access other nodes within the graph. Although full access to the underlying graph is available to any of the scripts, PathQL provides a succinct, and efficient method to access directly or indirectly related nodes. PathQL can either return just the contents of the referenced nodes, or the contents and the path to the referenced nodes. PathQL can also be used standalone to query the IntelligentGraph-enabled RDF database. This supplements, rather than replaces, SPARQL and GraphQL, as it provides graph-path querying rather than graph-pattern querying capabilities to any IntelligentGraph-enabled RDF database. The principles of IntelligentGraph are described here: https://inova8.com/bg_inova8.com/intelligent-graph-knowledge-graph-embedded-analysis/ The full PathQL syntax is described here: https://inova8.com/bg_inova8.com/pathpatternql-intelligently-finding-knowledge-as-a-path-through-a-maze-of-facts/ Using Jupyter as an IDE to IntelligentGraph and RDF4J, shown here: https://inova8.com/bg_inova8.com/intelligentgraph-getting-started/ IntelligentGraph source is here in GitHub: https://github.com/peterjohnlawrence/com.inova8.intelligentgraph IntelligentGraph Docker containers are available here: https://hub.docker.com/repository/docker/inova8/intelligentgraph