openhcs 0.5.19

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      |_|           High-Content Screening

🎬 Demo

Watch demo in browser player: https://openhcs.readthedocs.io/en/latest/_static/openhcs.mp4
Mirror link (GitHub raw): https://raw.githubusercontent.com/OpenHCSDev/OpenHCS/refs/heads/main/docs/source/_static/openhcs.mp4

OpenHCS processes large microscopy datasets (100GB+) with a compile-then-execute architecture. Pipelines are validated across all wells before any processing starts, preventing failures after hours of computation. Design pipelines in the GUI, export to Python, edit as code, and re-import — switching seamlessly between visual and programmatic workflows.

graph LR
    subgraph Microscopes
        IX[ImageXpress]
        OP[Opera Phenix]
        OM[OMERO]
    end

    subgraph OpenHCS Platform
        PD["Pipeline Designer<br/>(GUI ⇄ Code ⇄ LLM)"]
        CO["5-Phase Compiler<br/>(validate)"]
        EX["Multi-Process Executor<br/>(1 process/well · multi-GPU)"]
        FN["574+ Unified Functions<br/>scikit-image · CuPy · pyclesperanto<br/>PyTorch · JAX · TF · CuCIM · custom"]
        PS["PolyStore<br/>(Memory ↔ Disk ↔ ZARR ↔ Stream)"]
    end

    subgraph Viewers
        NA[Napari]
        FJ[Fiji/ImageJ]
    end

    IX --> PD
    OP --> PD
    OM --> PD
    PD --> CO --> EX
    EX --> FN --> PS
    PS --> NA
    PS --> FJ

⚡ Key Capabilities

🧩 The OpenHCS Ecosystem

OpenHCS is built on 8 purpose-extracted libraries — each solving a general problem, each independently publishable, all woven into a cohesive platform:

graph TD
    OH["OpenHCS Platform<br/>(domain wiring + pipelines)"]

    OH --> OS["ObjectState<br/>(config)"]
    OH --> AB["ArrayBridge<br/>(arrays)"]
    OH --> PS["PolyStore<br/>(I/O + streaming)"]
    OH --> ZR["ZMQRuntime<br/>(exec)"]
    OH --> QR["PyQT-reactive<br/>(forms)"]

    OS --> PI["python-introspect<br/>(signatures)"]
    OH --> MR["metaclass-registry<br/>(plugins)"]
    OH --> PC["pycodify<br/>(serialization)"]

🔬 Microscope & Function Support

Processing domains: image preprocessing · segmentation · cell counting · stitching (MIST + Ashlar GPU) · neurite tracing · morphology · measurements

🚀 Quick Start

# Basic installation with GUI
pip install openhcs[gui]

# Add Napari viewer
pip install openhcs[gui,napari]

# Add Fiji/ImageJ viewer
pip install openhcs[gui,fiji]

# Add both viewers
pip install openhcs[gui,viz]

# Add GPU acceleration (CUDA 12.x required)
pip install openhcs[gui,gpu]

# Full installation (GUI + viewers + GPU)
pip install openhcs[gui,viz,gpu]

# Launch the application
openhcs

# Or use programmatically — real pipeline from a neuroscience experiment
from openhcs.core.orchestrator.orchestrator import PipelineOrchestrator
from openhcs.core.steps.function_step import FunctionStep
from openhcs.constants.constants import VariableComponents
from openhcs.processing.backends.processors.cupy_processor import (
    stack_percentile_normalize, tophat, create_composite
)
from openhcs.processing.backends.pos_gen.ashlar_main_gpu import ashlar_compute_tile_positions_gpu
from openhcs.processing.backends.assemblers.assemble_stack_cupy import assemble_stack_cupy
from openhcs.processing.backends.analysis.cell_counting_cpu import count_cells_single_channel

steps = [
    FunctionStep(func=[
        (stack_percentile_normalize, {'low_percentile': 1.0, 'high_percentile': 99.0}),
        (tophat, {'selem_radius': 50, 'downsample_factor': 4})
    ], name="preprocess", variable_components=[VariableComponents.SITE]),

    FunctionStep(func=[create_composite],
                 name="composite", variable_components=[VariableComponents.CHANNEL]),

    FunctionStep(func=[ashlar_compute_tile_positions_gpu],
                 name="find_positions", variable_components=[VariableComponents.SITE]),

    FunctionStep(func=[(assemble_stack_cupy, {'blend_method': 'fixed'})],
                 name="assemble", variable_components=[VariableComponents.SITE],
                 force_disk_output=True),

    FunctionStep(func=[count_cells_single_channel],
                 name="count_cells", variable_components=[VariableComponents.SITE]),
]

orchestrator = PipelineOrchestrator("path/to/plate")
orchestrator.initialize()
compiled = orchestrator.compile_pipelines(steps)  # Validates everything first
orchestrator.execute_compiled_plate(steps, compiled, max_workers=5)

pip install openhcs              # Headless (servers, CI)
pip install openhcs[gui]         # Desktop GUI
pip install openhcs[gui,napari]  # GUI + Napari viewer
pip install openhcs[gui,viz]     # GUI + Napari + Fiji
pip install openhcs[gui,viz,gpu] # Full installation
pip install openhcs[gpu]         # Headless + GPU
pip install openhcs[omero]       # OMERO integration
python scripts/dev_install.py --extras all,dev  # Development from pinned submodules

pip install 'openhcs[omero]'     # Auto-installs zeroc-ice

python scripts/install_omero_deps.py   # Standalone script
pip install -r requirements-omero.txt  # Requirements file

📖 Documentation

⚙️ Architecture Highlights

Define → Compile → Freeze → Execute
         ├─ 1. Path planning
         ├─ 2. ZARR store declaration
         ├─ 3. Materialization flags
         ├─ 4. Memory contract validation
         └─ 5. GPU assignment
              → context.freeze()  # immutable

Function patterns · Individual steps · Pipeline configs · Full orchestrator scripts
              ↕  generate / AST-parse back  ↕

🤝 Contributing

git clone --recurse-submodules https://github.com/OpenHCSDev/OpenHCS.git && cd OpenHCS
python scripts/dev_install.py --extras all,dev
pytest tests/

Contribution areas: microscope formats · processing functions · GPU backends · documentation

📄 License

MIT — see LICENSE.

🙏 Acknowledgments

OpenHCS evolved from EZStitcher and builds on Ashlar (stitching), MIST (phase correlation), pyclesperanto (GPU image processing), and scikit-image (image analysis).