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PyNipe\n\n[![CI/CD](https://github.com/vanandrew/pynipe/actions/workflows/workflow.yml/badge.svg)](https://github.com/vanandrew/pynipe/actions/workflows/workflow.yml)\n[![Python Versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)](https://github.com/vanandrew/pynipe)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nA neuroimaging workflow library that builds upon Nipype's excellent interfaces while providing a more intuitive and debuggable execution model with support for parallel execution.\n\n## Overview\n\nPyNipe enables neuroimaging researchers to create processing pipelines using natural Python flow, making development and debugging significantly easier while maintaining the ability to run tasks in parallel.\n\n## Key Features\n\n- **Reuse Nipype interfaces**: Leverage the mature interface implementations from Nipype\n- **Natural Python flow**: Enable standard Python control structures (if/else, loops, etc.)\n- **Improved debugging**: Make it easy to see what's happening at each step\n- **Modular processing**: Build pipelines from reusable processing functions\n- **Parallel execution**: Support concurrent execution of independent tasks\n- **Delayed execution**: Configure tasks first, execute later through the executor\n- **Automatic dependency tracking**: Dependencies between tasks are automatically tracked\n- **Integration with pipeline tools**: Support modern pipeline management tools like Airflow\n\n## Installation\n\n```bash\npip install pynipe\n```\n\n## Basic Usage\n\n```python\nfrom nipype.interfaces import fsl\nfrom pynipe import TaskContext, Workflow, LocalExecutor\n\n# Define a processing function\ndef process_subject(subject_id, anat_file, output_dir):\n    # Brain extraction task\n    with TaskContext(\"Brain Extraction\") as ctx:\n        # Create and configure the interface\n        bet = fsl.BET()\n        ctx.set_interface(bet)\n\n        # Configure the interface parameters\n        ctx.configure_interface(\n            in_file=anat_file,\n            out_file=f\"{output_dir}/{subject_id}_brain.nii.gz\",\n            mask=True,\n            frac=0.3\n        )\n\n        # Get output proxies for later use\n        outputs = ctx.get_output_proxy()\n        brain_file = outputs.outputs.out_file\n        mask_file = outputs.outputs.mask_file\n\n    return {\n        \"brain\": brain_file,\n        \"mask\": mask_file\n    }\n\n# Create a workflow\nworkflow = Workflow(\"Simple Pipeline\")\n\n# Add processing for a subject\nworkflow.add_function(\n    process_subject,\n    inputs={\n        \"subject_id\": \"sub-01\",\n        \"anat_file\": \"/path/to/sub-01/anat.nii.gz\",\n        \"output_dir\": \"/path/to/output\"\n    }\n)\n\n# Run the workflow with parallel execution\nexecutor = LocalExecutor(max_workers=2)\nresults = workflow.run(executor=executor)\n\n# Access task information after execution\nfor task_name, task_outputs in results[\"tasks\"].items():\n    task = workflow.get_task_by_name(task_name)\n    if task:\n        print(f\"Task: {task_name}\")\n        print(f\"Status: {task.status}\")\n        print(f\"Execution time: {task.elapsed_time:.2f}s\")\n        print(f\"Command: {task.command}\")\n```\n\n## License\n\n[MIT License](LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanandrew%2Fpynipe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvanandrew%2Fpynipe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanandrew%2Fpynipe/lists"}