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Platform Status DB Documentation

Platform Status DB is a Django-based database system designed for tracking and managing the status of robotic laboratory automation platforms. It implements the interface required by the laborchestrator package for process logging, container tracking, and device management.

What is Platform Status DB?

Platform Status DB provides a centralized database for:

  • Device Management: Track laboratory devices (robotic arms, plate readers, incubators, etc.) and their available positions
  • Container Tracking: Monitor laboratory containers (plates, tubes, etc.) as they move through the platform
  • Process Logging: Record process steps, experiments, and their execution details
  • Duration Estimation: Predict process step durations based on historical data
  • Web Interface: View and manage platform status through Django admin and custom views

Key Features

  • Real-time Container Tracking: Know the current location of every container on your platform
  • Movement History: Full audit trail of all container movements
  • Process Step Recording: Detailed logs of all operations with timing information
  • Barcode Support: Track containers using barcode identifiers
  • Lid Management: Track lidding/unlidding operations and lid locations
  • Historical Analysis: Duration estimation for future operations based on past performance
  • REST-like Interface: Programmatic access through Python API
  • Web Dashboard: Visual interface for monitoring platform status

Architecture Overview

The system consists of two main Django applications:

  1. larastatus: Core implementation and configuration
  2. Implements StatusDBInterface from laborchestrator
  3. Provides duration estimation capabilities

  4. Django project configuration

  5. job_logs: Data models and web views

  6. Database models for devices, containers, and processes
  7. Web views for visualizing platform status
  8. Django admin interface customization

Use Cases

Laboratory Automation

Track containers through complex multi-step laboratory workflows, ensuring traceability and monitoring system utilization.

Process Optimization

Analyze historical data to identify bottlenecks and optimize workflow scheduling.

Compliance and Auditing

Maintain detailed logs of all operations for regulatory compliance and troubleshooting.

Integration with Orchestration Systems

Provide real-time status information to laboratory orchestration systems for intelligent decision-making.

Documentation Structure

Quick Example

from platform_status_db.larastatus.status_db_implementation import StatusDBImplementation
from laborchestrator import structures

# Initialize the database
db = StatusDBImplementation()

# Create lab from configuration file
db.create_lab_from_config("lab_config.yaml")

# Add a container
container_info = structures.ContainerInfo(
    name="Plate001",
    current_device="RoboticArm1",
    current_pos=0,
    barcode="BC12345",
    lidded=True,
    filled=True
)
db.add_container(container_info)

# Track a movement
db.moved_container(
    source_device="RoboticArm1",
    source_pos=0,
    target_device="PlateReader",
    target_pos=1,
    barcode="BC12345"
)

# Query container location
container = db.get_cont_info_by_barcode("BC12345")
print(f"Container is at {container.current_device}, position {container.current_pos}")

System Requirements

  • Python 3.9 or higher
  • Django 4.1+
  • laborchestrator package (<0.3)
  • SQLite (default) or other Django-supported databases

License

MIT License - See LICENSE file for details.

Support

For issues and questions: - GitLab Issues: Platform Status DB Issues - Documentation: Online Documentation