PyAMA-ACDC

PyAMA-ACDC is a Cell-ACDC integration plugin that exposes PyAMA workflow helpers inside Cell-ACDC’s Qt welcome window, allowing seamless access to PyAMA’s advanced analysis capabilities from within the Cell-ACDC environment.

Purpose

The plugin enables Cell-ACDC users to:

Access PyAMA workflows from Cell-ACDC’s interface
Run PyAMA’s advanced segmentation and tracking
Transition smoothly between Cell-ACDC and PyAMA analysis
Use PyAMA as an option in Cell-ACDC’s modular system

Installation

# Install as part of the PyAMA workspace
uv pip install -e pyama-acdc/

# For standalone installation (in Cell-ACDC environment)
pip install pyama-acdc

Architecture

The plugin exposes a minimal API designed to integrate with Cell-ACDC’s plugin system:

from pyama_acdc import pyAMA_Win

# Main integration point - PyAMA window that can be launched from Cell-ACDC
class pyAMA_Win(QWidget):
    """Main PyAMA workflow window for Cell-ACDC integration."""

Usage in Cell-ACDC

Basic Integration

# In Cell-ACDC's main window
from pyama_acdc import pyAMA_Win

class mainWin(QMainWindow):
    def showPyamaWorkflow(self):
        """Launch PyAMA workflow from Cell-ACDC."""
        win = pyAMA_Win(self)
        win.show()

Advanced Integration

# Direct API access
from pyama_acdc._run import run_gui, icon_path, logo_path

# Get assets for Cell-ACDC launcher
icon = icon_path()
logo = logo_path()

# Launch with configuration
run_gui(parent=cell_acdc_window, config_path="path/to/config.yaml")

Workflow Integration

PyAMA Workflow Steps in Cell-ACDC Context

The integrated workflow follows a Cell-ACDC-friendly sequence:

Step 0 - Cell-ACDC’s native “Create data structure” dialog
- Initializes experiment folders
- Sets up standard Cell-ACDC directory structure
Step 1 - PyAMA segmentation module
- Runs LOG-STD segmentation immediately after structuring
- Creates tracked cell IDs
- Exports CSV traces
Step 2 - PyAMA data prep module
- Handles background correction
- Applies quality filters
- Prepares for analysis
Step 3 - PyAMA analysis GUI
- Model fitting interface
- Parameter analysis
- Export capabilities

File Integration

cell_acdc_experiment/
├── Images/              # Cell-ACDC images
├── pyama/               # PyAMA outputs
│   ├── processing_config.yaml
│   ├── fov_XXX/
│   └── *_traces.csv
└── analysis/            # Combined analysis outputs

Configuration

Cell-ACDC Plugin Registration

The plugin registers with Cell-ACDC’s system by exposing:

# Required for Cell-ACDC discovery
MODULE_NAME = "PyAMA Analysis"
MODULE_VERSION = "1.0.0"
MODULE_DESCRIPTION = "Advanced microscopy analysis workflow"

Integration Points

# pyama_acdc/__init__.py

# Entry points for Cell-ACDC
icon_path: str  # Path to PyAMA icon
logo_path: str  # Path to PyAMA logo
run_gui: Callable # Launch PyAMA GUI

# Configuration
def get_config():
    """Return configuration for Cell-ACDC integration."""
    return {
        "workflow_type": "pyama",
        "compatible_versions": ["1.0.0"],
        "requires_nd2": True,
        "supports_tracking": True
    }

API Reference

Main Classes

class pyAMA_Win(QWidget):
    """Main PyAMA window launched from Cell-ACDC."""
    
    def __init__(self, parent=None):
        """Initialize with optional Cell-ACDC parent window."""
        
    def load_from_cell_acdc(self, exp_path):
        """Load Cell-ACDC experiment data."""
        
    def export_to_cell_acdc_format(self):
        """Export results to Cell-ACDC compatible format."""

Utility Functions

def run_gui(parent=None, config_path=None):
    """Launch PyAMA GUI with optional configuration."""
    
def get_available_workflows():
    """Return list of PyAMA workflows compatible with Cell-ACDC."""
    
def validate_cell_acdc_structure(path):
    """Check if path follows Cell-ACDC structure."""

Migration Path

From Cell-ACDC to PyAMA

Data Structure:
- Cell-ACDC organizes: Images/Position_X/Time_Y/
- PyAMA uses: Single ND2 with FOV metadata
- Plugin handles conversion automatically
Segmentation:
- Cell-ACDC: Manual or basic algorithms
- PyAMA: LOG-STD with IoU tracking
- Plugin imports Cell-ACDC masks if available
Analysis:
- Cell-ACDC: Basic measurements
- PyAMA: Advanced model fitting
- Plugin exports results to both formats

Hybrid Workflows

# Use Cell-ACDC for initial processing
cell_acdc.segmentation.run(image_path)

# Continue with PyAMA analysis
pyama_Win.load_from_cell_acdc(exp_path)
pyama_Win.run_analysis()

Examples

Basic Usage Example

from pyama_acdc import pyAMA_Win
from cell_acdc import main as cell_acdc

# In Cell-ACDC application
def launch_pyama_analysis():
    # Get current experiment
    exp_path = cell_acdc.get_current_experiment()
    
    # Launch PyAMA
    pyama_window = pyAMA_Win(parent=cell_acdc.main_window)
    pyama_window.load_from_cell_acdc(exp_path)
    pyama_window.show()

Data Integration Example

# Analyze Cell-ACDC data with PyAMA
def analyze_with_pyama(cell_acdc_path):
    from pyama_acdc.helpers import convert_cell_acdc_to_pyama
    
    # Convert data structure
    pyama_path = convert_cell_acdc_to_pyama(cell_acdc_path)
    
    # Run PyAMA analysis
    from pyama.processing.analysis import run_complete_analysis
    results = run_complete_analysis(pyama_path)
    
    # Export back to Cell-ACDC format
    from pyama_acdc.export import to_cell_acdc_format
    to_cell_acdc_format(results, cell_acdc_path)

Development

Building the Plugin

# Install in development mode
uv pip install -e pyama-acdc/

# Test integration with Cell-ACDC
python -c "from pyama_acdc import pyAMA_Win; print('Plugin works')"

Testing Integration

# pyama-acdc/tests/test_integration.py (if created)
def test_cell_acdc_integration():
    """Test that plugin integrates correctly with Cell-ACDC."""
    from pyama_acdc import pyAMA_Win
    from PyQt6.QtWidgets import QApplication
    
    app = QApplication([])
    window = pyAMA_Win()
    assert window.windowTitle() == "PyAMA Workflow"

Debug Mode

# Enable debug logging
import pyama_acdc
pyama_acdc.set_debug_level("DEBUG")

# Check Cell-ACDC compatibility
from pyama_acdc.compatibility import check_cell_acdc_version
check_cell_acdc_version()

Troubleshooting

Common Issues

“Plugin not found in Cell-ACDC”:

Ensure pyama-acdc is in Python path
Check Cell-ACDC plugin directory configuration
Verify PyQt6 dependencies

“Data conversion failed”:

Check Cell-ACDC data structure
Verify image formats are supported
Ensure sufficient disk space

“Window won’t open”:

Check Qt application initialization
Verify parent window is valid
Check for Qt version conflicts

Debug Information

# Get diagnostic information
from pyama_acdc.diagnostics import get_system_info
info = get_system_info()
print(info)

# Check compatibility
from pyama_acdc.compatibility import CompatibilityReport
report = CompatibilityReport()
print(report.summary())

Best Practices

Data Organization

Keep Cell-ACDC and PyAMA outputs separate
Use consistent naming conventions
Document conversion steps
maintain both formats when possible

Workflow Integration

Start with Cell-ACDC for initial data organization
Use PyAMA for advanced analysis
Export results back to Cell-ACDC if needed
Keep track of data provenance

Performance

Preprocess data in Cell-ACDC when beneficial
Use PyAMA’s optimized algorithms for large datasets
Cache conversion results
Monitor memory usage during conversions

Future Enhancements

Direct data synchronization between Cell-ACDC and PyAMA
Shared parameter configurations
Unified analysis dashboard
Export to additional formats (Fiji, ImageJ)
Batch processing across multiple experiments

Resources

Cell-ACDC documentation: https://github.com/pycellacdc/pycellacdc
PyAMA user guide: User Guide
Integration examples in examples/ directory
Test data for development in package-specific test directories

PyAMA-ACDC bridges the gap between Cell-ACDC’s user-friendly interface and PyAMA’s advanced analytical capabilities, providing a seamless experience for microscopy image analysis workflows.