notebooks

This project contains example code for working with ImageJ and SciJava.

WHY JUPYTER NOTEBOOKS

Jupyter Notebooks are a great outreach tool to demonstrate how programs work interactively in real time. We use Jupyter for teaching ImageJ2 so that you can try and change the code yourself easily. See the Intro to Jupyter notebook if you have never used Jupyter notebook before.

RUNNING JUPYTER NOTEBOOKS

There are multiple ways to run the Jupyter Notebooks:

Method	Pros	Cons	Running Live?
Local Environment	Run code locally on your machine.	Must install developer tools.	Yes
On GitHub	View notebooks quickly on GitHub's website.	Code does not run live; rendering is less complete & correct than on nbviewer.	No
Binder	Run code on the cloud, no local installation.	Slow to spin up the web container.	Yes
Nbviewer	View notebooks nicely rendered on nbviewer.org.	Code does not run live; not integrated with GitHub.	No

(Recommended) Local Environment

1. Install Miniconda.
2. Clone this imagej/tutorials repository.
3. Open a console and cd to your cloned working copy.
4. conda env create -f environment.yml to create a conda environment with the dependencies these notebooks need.
5. conda activate scijava to activate the environment.
6. jupyter notebook to launch Jupyter Notebook in a web browser window.
7. In the browser, click into notebooks, then click on the ImageJ-Tutorials-and-Demo.ipynb notebook to open it.

On GitHub

The easiest way to get started with the ImageJ and SciJava APIs is via the ImageJ Jupyter notebooks, located in the notebooks subfolder of this repository.

Binder

Use the "launch binder" badge above to try the Jupyter notebooks on the cloud using Binder, with no local installation necessary.

Note: Binder startup may take a minute

Nbviewer

Use the "render nbviewer" badge above to access the Jupyter Notebooks on your machine through nbviewer.

Notebook Technologies

The introductory notebooks use the Groovy kernel from BeakerX. Several other JVM-based kernels are usable as well, including Clojure, Java, Kotlin and Scala.

There are also notebooks using the standard Python kernel plus the pyimagej package, enabling use of ImageJ from Python programs.

LICENSING

To the extent possible under law, the ImageJ developers have waived all copyright and related or neighboring rights to this tutorial code.

See unlicense.org for details.

SEE ALSO

• The Tutorials and Development sections of the ImageJ wiki.