Pegasus Wrapper

Step 3: Select Reference Pattern

Based on user answers, read the closest existing example:

Read the selected reference before generating code.

Step 4: Generate the Wrapper

For Python wrappers:

Start from pegasus-templates/wrapper_template.py and customize:

1. Docstring: Describe what this step does
2. argparse arguments: Must match what the workflow_generator.py will pass via add_args()
3. os.makedirs: Create output subdirectories before writing (any path with /)
4. Tool invocation: Use subprocess.run() for CLI tools, or call Python libraries directly
5. Exit code propagation: sys.exit(result.returncode) after subprocess
6. Structured logging: Use logging module with logger.info() for inputs, commands, and results
7. Output verification: Check the output file exists before exiting

For shell wrappers:

Start from pegasus-templates/wrapper_template.sh and customize:

1. set -euo pipefail: Always include
2. Argument parsing: case statement to extract named arguments
3. Tool execution: Call the tool with parsed arguments
4. Output flattening: Copy expected output files from nested directories to the working directory root
5. Headless handling (if needed): unset DISPLAY, xvfb-run fallback

Critical Rules

1. Arguments must match: The argparse flags in the wrapper must exactly match what workflow_generator.py passes in add_args(). Show the user both sides.
2. No directory scanning: Never use glob(), os.listdir(), list.files(), or find to discover input files. Accept them explicitly via arguments.
3. Support files via os.getcwd(): If the wrapper needs a support file (R script, JAR), find it with os.path.join(os.getcwd(), "filename") — NOT relative to __file__.
4. Create subdirectories: Any output path containing / needs os.makedirs(os.path.dirname(output), exist_ok=True).
5. Print the command: Always log the command being run — this is essential for debugging via pegasus-analyzer.

Step 5: Show Integration

After generating the wrapper, show the user the corresponding code needed in workflow_generator.py:

1. Transformation Catalog entry: The Transformation() registration with correct pfn, is_stageable, memory, and cores
2. Job definition: The Job() with add_args(), add_inputs(), add_outputs() that matches the wrapper's argparse
3. Replica Catalog entry (if the wrapper uses support files): rc.add_replica() for R scripts, JARs, etc.

This ensures the wrapper and workflow generator stay in sync.

Full Workflow Repositories

For complete wrapper scripts beyond the examples:

• https://github.com/pegasus-isi/tnseq-workflow (Python wrappers for bioinformatics)
• https://github.com/pegasus-isi/earthquake-workflow (API fetch wrappers)
• https://github.com/pegasus-isi/mag-workflow (shell wrappers with output flattening)