Macro Development Project for Behavioral Research Study

Introduction

Welcome to my Macro Development Project at the LeBow College of Business, a earnest effort to automate and streamline the operational aspects of behavioral research studies. This project involves developing a suite of Python-based automation tools that work together seamlessly to ensure efficient data collection and processing. The project leverages a custom-built Tkinter GUI to provide an intuitive control panel for experimenters.

Project Overview

The Macro Development Project integrates automation tools into a single executable, reducing manual processes and enhancing data integrity throughout the research lifecycle. The project's components include:

1. Research Control Panel (Ctrl_Experimenter_NEW.py):

   • A user-friendly GUI control panel built with Tkinter that allows experimenters to input team IDs and manage research sessions.

2. Session Management Tools:

   • Scripts for starting and stopping research sessions and simulations (Start_Artemis_M*.py, Stop_Artemis.py).

3. Data Capture Utilities:

   • Tools for taking timed screenshots and handling survey data (Take_Screenshot*.py, Start_Survey*.py).

4. Logistics and Archiving Features:

   • Macros for organizing and archiving mission logs (move_log_E.py) and video recordings (move_video_P.py).

5. Performance Metrics and Training:

   • Automation of performance metrics collection (Start_Recording_P.py, Stop_Recording_P.py) and setting up training sessions (Start_Training_P.py).

Advanced Features

The control panel integrates XML-RPC clients to communicate with remote stations during experiments. It handles complex operations such as:

• Verifying connections to participant stations.
• Running parallel tasks using threading to coordinate between different experiment phases.
• Starting and stopping recording sessions and simulations across multiple systems.

Future Directions

As I continue to develop this project, the immediate goals include:

• Integration: Combining all tools into a cohesive and unified system.
• Enhancement: Refining scripts for greater resilience and scalability.
• Usability: Ensuring the GUI is intuitive and accessible for all experimenters.

Contact Information

For any inquiries or further information regarding this project, please reach out to:

• Email: [[email protected]]
• Project Link: [https://github.com/invcble/LeBow-RA]