Basic kotlin (1.1.0-beta-38) 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 notebook output is here. (It is ported from Gral
project's ConvolutionExample.java
).
The notebook itself is located in the samples
folder.
The following REPL commands are supported:
:help
- displays REPL commands help:classpath
- displays current classpath
It is possible to add dynamic dependencies to the notebook using the following annotations:
@file:DependsOnJar(<relative|absolute-path-in-dir-repo>)
- adds a class directory or jar to the classpath@file:DirRepository(<absolute-path>)
- adds a directory as a repo@file:DependsOnMaven(<colon-separated-maven-coordinates>)
- resolves artifacts using maven, downloads them if necessary, and adds to the classpath@file:MavenRepository(<mavenRepoUrl>)
- adds a maven repository
Note: The maven repositories used are defaulted to Maven Centralas a remote repo and ~/.m2/repository
as a local one.
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).
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/
.
jupyter-console --kernel=kotlin
or
jupyter-notebook
and then create a new notebook with kotlin
kernel.
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.)
- 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
- additional jars for the REPL could be passed using
- run
jupyter-console
passing the full path to the same config file as an argument to the--existing
command line parameter