Kotlin kernel for iPython/Jupyter

Basic kotlin REPL kernel for jupyter (http://jupyter.org).

Autocompletion, history and other advanced features are not yet supported.

Alpha version. Tested only with jupyter 4.1.1 on OS X so far.

Example

Example notebook output is here. (It is ported from Gral project's ConvolutionExample.java).

The notebook itself is located in the samples folder.

Supported functionality

REPL commands

The following REPL commands are supported:

• :help - displays REPL commands help
• :classpath - displays current classpath

Dependencies resolving annotations

It is possible to add dynamic dependencies to the notebook using the following annotations:

• @file:DependsOn(<absolute-path>) - adds a class directory or jar to the classpath
• @file:DependsOn(<colon-separated-maven-coordinates>) - resolves artifacts using maven, downloads them if necessary, and adds to the classpath

Note: The repositories for maven resolving are fixed in this release - Maven Central is used as a remote repo and ~/.m2/repository as a local one.

MIME output

By default the return values from REPL statements are displayed in the text form. To use richer representations, e.g. to display graphics or html, it is possible to send MIME-encoded result to the client using the Result type and resultOf helper function. The latter has a signature:

fun resultOf(vararg mimeToData: Pair<String, Any>): Result 

E.g.:

resultOf("text/html" to "<p>Some <em>HTML</em></p>", "text/plain" to "No HTML for text clients")

(See also toSvg function in the example).

Installation

Run ./gradlew install

Use option -PinstallPath= to specify installation path. (Note that jupyter looks for kernel specs files only in predefined places.)

Default installation path is ~/.ipython/kernels/kotlin/.

Usage

jupyter-console --kernel=kotlin

or

jupyter-notebook

and then create a new notebook with kotlin kernel.

Additional libraries

In addition to using resolving annotations, jars could be added directly to the REPL using -cp= parameter in argv list in the installed kernel.json file. Standard classpath format is used. (Please make sure to use only absolute paths in the kernel.json file.)

Debugging

• run kernel jar passing some connection config file as a parameter, e.g. testData/config.json
  • additional jars for the REPL could be passed using -cp= parameter
• run jupyter-console passing the full path to the same config file as an argument to the --existing command line parameter