Welcome to pyDataPipeline’s documentation!

Contents:

• Installation
• SEIRS Model example
  • config.yaml
  • fair pull
  • fair run
  • Output
  • Provenance Report
• data_pipeline_api
  • finalise()
  • get_handle_index_from_path()
  • initialise()
  • link_read()
  • link_write()
  • raise_issue_by_data_product()
  • raise_issue_by_existing_data_product()
  • raise_issue_by_index()
  • raise_issue_by_type()
  • raise_issue_with_config()
  • raise_issue_with_github_repo()
  • raise_issue_with_submission_script()
• SCRC Software checklist
  • Software details
    • Model / software name
    • Date
    • Version identifier
  • This assessment
    • Filled in by
    • Overall statement
  • Checklist
    • Can a run be repeated and reproduce exactly the same results? (models only)
    • Are there appropriate tests? (And are they automated?)
    • Are the scientific results of runs robust to different ways of running the code? (models only)
    • Has any sort of automated code checking been applied?
    • Is the code clean, generally understandable and readable and written according to good software engineering principles?
    • Is there sufficient documentation?
    • Is there suitable collaboration infrastructure?
    • Are software dependencies listed and of appropriate quality?
    • Is input and output data handled carefully? (Models only)
  • Contributors and licence