Cheat Sheet¶

The ruffus module is a lightweight way to add support for running computational pipelines.

Each stage or task in a computational pipeline is represented by a python function

Each python function can be called in parallel to run multiple jobs.

1. Annotate functions with Ruffus decorators¶

Decorator	Syntax
@originate (Manual)	@originate ( `output_files`, [`extra_parameters`,...] )
@split (Manual)	@split ( `tasks_or_file_names`, `output_files`, [`extra_parameters`,...] )
@transform (Manual)	@transform ( `tasks_or_file_names`, suffix(`suffix_string`), `output_pattern`, [`extra_parameters`,...] ) @transform ( `tasks_or_file_names`, regex(`regex_pattern`), `output_pattern`, [`extra_parameters`,...] )
@merge (Manual)	@merge (`tasks_or_file_names`, `output`, [`extra_parameters`,...] )
@posttask (Manual)	@posttask ( `signal_task_completion_function` ) @posttask (touch_file( `'task1.completed'` ))

For a graphical flowchart in jpg, svg, dot, png, ps, gif formats:
```
pipeline_printout_graph ( "flowchart.svg")
```

pipeline_run(multiprocess = N_PARALLEL_JOBS)