Integration Guides

These guides explain how OpenHCS systems work together to provide seamless bioimage analysis workflows. Each guide focuses on the integration between major system components.

Complete Examples

Complete OpenHCS Examples

Working Examples: Comprehensive scripts demonstrating major OpenHCS functionality and practical usage patterns.

Complete OpenHCS Examples

System Integration Guides

Memory Type Integration

Learn how OpenHCS automatically converts between NumPy, CuPy, PyTorch, JAX, TensorFlow, and pyclesperanto arrays. Understand GPU device management, zero-copy conversions, and memory type decorators.

Memory Type System Integration

Pipeline Compilation Workflow

Understand the 5-phase compilation system that transforms pipeline definitions into optimized execution plans. Learn about path planning, materialization, memory contract validation, and GPU resource assignment.

Pipeline Compilation Workflow

OMERO Integration

Complete server-side execution support for OpenHCS on OMERO servers with zero data transfer overhead. Learn about virtual backends, multiprocessing-safe connection management, and automatic instance management.

OMERO Integration

Viewer Management

Learn how to manage Napari and Fiji viewer processes across OpenHCS components. Understand viewer reuse, parallel startup, persistent viewers, and automatic reconnection for real-time visualization workflows.

Viewer Management Guide

Fiji Viewer Management

Learn how to manage Fiji/ImageJ viewer processes for OpenHCS visualization. Understand PyImageJ integration, automatic hyperstack building, persistent viewers, and ZMQ-based streaming for leveraging the ImageJ ecosystem.

Fiji Viewer Management

Testing Guide

Comprehensive guide for running OpenHCS integration tests with different configurations. Learn how to test with Napari/Fiji visualizers, OMERO backend, and various execution modes using VSCode test discovery or command-line.

Testing Guide

Quick Reference

Memory Type Decorators:

from openhcs.core.memory.decorators import numpy, cupy, torch, jax, pyclesperanto

@numpy
def cpu_function(image): pass

@torch(oom_recovery=True)
def gpu_function(image): pass

Pipeline Compilation:

# Automatic compilation in orchestrator
orchestrator = PipelineOrchestrator(
    plate_paths=plate_paths,
    steps=steps,
    global_config=global_config
)

# 5-phase compilation happens automatically
orchestrator.run()

Function Patterns:

# Single function
FunctionStep(func=my_function)

# Function with parameters
FunctionStep(func=(my_function, {'param': value}))

# Function chain
FunctionStep(func=[
    (func1, {'param1': value1}),
    (func2, {'param2': value2})
])

# Dict pattern (channel-specific)
FunctionStep(func={
    '1': nuclei_function,
    '2': neurite_function
}, variable_components=[VariableComponents.CHANNEL])

Integration Patterns

Memory Type + Function Registry: Functions are automatically discovered and registered with their memory type contracts, enabling automatic conversion planning during compilation.

Pipeline Compilation + GPU Management: The compilation system assigns GPU resources and validates memory requirements before execution begins.

Special I/O + VFS System: Cross-step communication uses the VFS system for efficient data transfer between pipeline steps.

Configuration + All Systems: The configuration system provides unified settings that affect memory management, compilation, and execution across all components.