{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Welcome to the Scijava Jupyter Kernel\n", "\n", "*If you haven't checked the introduction and installation instructions of the `scijava-jupyter-kernel` yet, please [click here](https://github.com/scijava/scijava-jupyter-kernel).*\n", "\n", "The goal of this series of notebooks is to introduce you to some of the features of the `scijava-jupyter-kernel`.\n", "\n", "Note that those notebooks are not an introduction course to the diverse languages supported by the `scijava-jupyter-kernel`. Neither it's an introduction to imaging analysis with ImageJ. Check the amazing [ImageJ website](https://imagej.net/) for this.\n", "\n", "---\n", "\n", "A list of notebooks highlighting the **Scijava Jupyter Kernel** specific features : \n", "\n", "- [Polyglot](./Polyglot.ipynb) : Introduction to the polyglot capabilities of the kernel.\n", "- [Rich Output](./Rich Output.ipynb) : Automatic output conversion. \n", "- [On-The-Fly Grabbing](./On-The-Fly Grabbing.ipynb) : How to dynamically download and load libraries ?\n", "- [Scijava](./Scijava.ipynb) : Scijava specific features.\n", "- [ImageJ](./ImageJ.ipynb) : ImageJ specific features.\n", "\n", "---\n", "\n", "*Help is always welcome to improve those notebooks. Don't hesitate [to submit your ideas](https://github.com/scijava/scijava-jupyter-kernel/pulls).*" ] } ], "metadata": { "kernelspec": { "display_name": "Scijava", "language": "groovy", "name": "scijava" }, "language_info": { "codemirror_mode": "groovy", "file_extension": "", "mimetype": "", "name": "scijava", "nbconverter_exporter": "", "pygments_lexer": "groovy", "version": "1.0" } }, "nbformat": 4, "nbformat_minor": 2 }