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

Core

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' ))

See Decorators for a complete list of decorators

2. Print dependency graph if necessary

• For a graphical flowchart in jpg, svg, dot, png, ps, gif formats:

  pipeline_printout_graph ( "flowchart.svg")
  

• For a text printout of all jobs

  pipeline_printout()
  

3. Run the pipeline

pipeline_run(multiprocess = N_PARALLEL_JOBS)