pynipe 0.1.0

A neuroimaging workflow library that builds upon Nipype's interfaces

PyNipe

A neuroimaging workflow library that builds upon Nipype's excellent interfaces while providing a more intuitive and debuggable execution model with support for parallel execution.

Overview

PyNipe 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.

Key Features

• Reuse Nipype interfaces: Leverage the mature interface implementations from Nipype
• Natural Python flow: Enable standard Python control structures (if/else, loops, etc.)
• Improved debugging: Make it easy to see what's happening at each step
• Modular processing: Build pipelines from reusable processing functions
• Parallel execution: Support concurrent execution of independent tasks
• Delayed execution: Configure tasks first, execute later through the executor
• Automatic dependency tracking: Dependencies between tasks are automatically tracked
• Integration with pipeline tools: Support modern pipeline management tools like Airflow

Installation

pip install pynipe

Basic Usage

from nipype.interfaces import fsl
from pynipe import TaskContext, Workflow, LocalExecutor

# Define a processing function
def process_subject(subject_id, anat_file, output_dir):
    # Brain extraction task
    with TaskContext("Brain Extraction") as ctx:
        # Create and configure the interface
        bet = fsl.BET()
        ctx.set_interface(bet)

        # Configure the interface parameters
        ctx.configure_interface(
            in_file=anat_file,
            out_file=f"{output_dir}/{subject_id}_brain.nii.gz",
            mask=True,
            frac=0.3
        )

        # Get output proxies for later use
        outputs = ctx.get_output_proxy()
        brain_file = outputs.outputs.out_file
        mask_file = outputs.outputs.mask_file

    return {
        "brain": brain_file,
        "mask": mask_file
    }

# Create a workflow
workflow = Workflow("Simple Pipeline")

# Add processing for a subject
workflow.add_function(
    process_subject,
    inputs={
        "subject_id": "sub-01",
        "anat_file": "/path/to/sub-01/anat.nii.gz",
        "output_dir": "/path/to/output"
    }
)

# Run the workflow with parallel execution
executor = LocalExecutor(max_workers=2)
results = workflow.run(executor=executor)

# Access task information after execution
for task_name, task_outputs in results["tasks"].items():
    task = workflow.get_task_by_name(task_name)
    if task:
        print(f"Task: {task_name}")
        print(f"Status: {task.status}")
        print(f"Execution time: {task.elapsed_time:.2f}s")
        print(f"Command: {task.command}")

License

MIT License