These tutorials are intended to familiarize the reader with SciJava Ops. In general, the Python use-cases are more targeted towards users, while Java is more developer focused.
You may wish to use ops from a jupyter notebook or simply
a python interpreter; in each case we recommend starting from the provided environment.yml.
To create and activate this environment, from the scijava-ops-tutorial directory in the terminal of your choice, run:
mamba env create
mamba activate scijava-opsJupyter notebooks are one of the easiest ways to interact with python code. The notebook we have provided is intended to introduce you to python programming in ops. You can simply open it from the command line via:
jupyter notebook notebooks/SciJavaOps.ipynbWe recommend using the ipython interpreter, as it includes many conveniences by default. In particular, tab completion is very useful when exploring available ops.
From the command line, start your interpreter in interactive mode and execute the intro script:
ipython -i scripts/ops-setup.pyThis will create a new ops object in your environment that can then be used to explore available ops. The easiest way
to do this is via tab completion.
For example, ops.<TAB> will show all available namespaces and global ops (ops with no namespace):
As you go into a particular namespace you can tab-complete to see the ops therein, e.g. ops.math.<TAB>:
You can get more information about the actual available implementations for a given Op, or all available ops, via
the help method:
# Print all available Ops
ops.help()
# Print all math.add Ops
ops.help("math.add")You can also access an OpEnvironment via the env variable, for a Java-style API.
The OpsIntro class is designed to take a developer through the basic concepts of the SciJava Ops framework. It includes examples of Ops calls, how to interrogate available Ops, and additional commentary about the framework.
Moving beyond the intro, the additional classes in the tutorial package provide focused guides to particular tasks or concepts.