Note: If you haven't read the CONTRIBUTING.md instructions, make sure to do so before continuing.
To add a new input source + sink to papermill look in the iorw.py file for a few examples.
The papermill_io object at root context of the module holds the registered IO handlers. This maintains the LIFO queue of potential path consumers for any IO request. Each handler is registered to a prefix path which it uses as a prefix match against the input arguments. You'll notice local is the lowest order handler and is a special case we use to fall-back on if no other handler matches.
To add a new handler, simply create a new class which implements the read, listdir, write, and pretty_path class methods. listdir is optional, and not used by the execute method / cli command. Then add papermill_io.register("my-new-prefix", MyNewHandler) and papermill will pick up the new IO handler for any paths it processes.
Remember to add tests to prove your new handler works!
Note: You can also extend the registry in your own modules to enable domain specific io extensions without needing to commit to papermill.
When you wish to add a new language or kernel to papermill, look in the translators.py file.
Like with the iorw pattern, there is a papermill_translators object at the root of the file which holds all key-value mappings from kernel / language names to translators. Each Translator inherits from Translator which gives the basic JSON conversion structures. Then for each JSON type you'll need to add the relevant translate_type class method. Additionally, you'll want to implement the comment function for mapping single line comments. For languages which have a special format for assigning variables you can also override the assign method (see ScalaTranslator for an example).
Finally, register the new handler to the papermill_translators object. The translator name must either match the kernel or language name being processed to be used for your notebook execution. This will enable any notebook using the named kernel to use your new parameter translations.
Test additions are easy to create -- just copy the few language specific pytest methods in test_translators.py and swap to your new translator name / expected values.
By default papermill uses nbconvert to process notebooks. But it's setup as a plug-n-play system so any function that can process a notebook and return the output nbformat object can be registered into papermill.
To enable a new engine, first look in engines.py at the NBConvertEngine as a working example. This class inherits from Engine and is required to implement the classmethod execute_managed_notebook. The first argument to this method is a NotebookExecutionManager -- which is built and passed in the Engine execute_notebook classmethod -- and is used to provide callback bindings for cell execution signals.
The NotebookExecutionManager class tracks the notebook object in progress, which is copied from the input notebook to provide functional execution isolation. It also tracks metadata updates and execution timing. In general you don't need to worry about this class except to know it has a nb attribute and three callbacks you can call from your engine implementation.
cell_starttakes a cell argument and sets the cell metadata up for execution. This triggers a notebook save.cell_exceptiontakes a cell argument and flags the cell as failed. This does not trigger a notebook save (as the notebook completion after cell failure will save).cell_completetakes a cell argument and finalizes timing information in the cell metadata. This triggers a notebook save.
These functions can be optionally called to better render and populate notebooks with appropriate metadata attributes to reflect their execution. Manually saving the notebook object is unnecessary as the base class wrapper will save the notebook on notebook start and completion on your behalf. If you wish to disable saving, overwrite the wrap_and_execute_notebook and prevent the output_path from propagating to the base method call.
papermill.execute_notebook allows you to pass arbitrary arguments down to the engine. Make sure that engine handles keyword arguments properly. Use utility merge_kwargs and remove_args to merge and clean arguments.
To update tests you'll need to add a new test class in test_engines.py. Copying the TestNBConvertEngine class and modifying it is recommended.
When adding an option to papermill, first look in cli.py and then execute.py for the two places to add your new configurable.
In cli.py you'll want to add an @click.option above the papermill method and the option name as a positional argument in the papermill method. These are fairly straight forward to assign, and you can refer to click's documentation for how to do all the basic options. Then you'll need to pass the argument to execute_notebook. We treat the CLI layer as a very light wrapper on the execute method in an attempt to both obey DRY (don't repeat yourself) and to ensure that the imported python module has the same capabilities as the CLI.
Now in execute.py's execute_notebook we want to add the appropriate argument and default it to something sane. Add the argument to the docstring as well. Then pass or use that argument where it's needed to achieve the desired effect. Usually these options get passed to _execute_parameterized_notebook.
To update the tests you'll need both test_cli.py and test_execute.py to include the new option. Though the CLI tests need only check that the appropriate values get passed to execute_notebook.