🩸 Trauma Model in PyTorch 🧬

Introduction ✨

This repository contains a refactored implementation of a trauma model originally written in a domain-specific language for biological modeling. The model simulates the complex interplay of hemodynamics, coagulation, and inflammation following a traumatic injury.

This PyTorch version offers significant advantages over the original implementation, including:

• 🚀 Enhanced Performance: Leveraging PyTorch's tensor operations and potential for GPU acceleration, this version runs significantly faster, enabling more efficient simulations and analysis.
• 🧱 Improved Modularity: The code is organized into separate modules for hemodynamics, coagulation, inflammation, and utility functions, promoting code reusability, readability, and maintainability.
• ⚙️ Centralized Configuration: Model parameters and settings are defined in a config.yaml file, making it easy to modify and experiment with different scenarios.
• 📝 Comprehensive Documentation: Detailed docstrings and inline comments provide clear explanations of the code, making it easier to understand and extend.

Getting Started 🚀

1. Installation 🧰

• Make sure you have Python 3.7 or higher installed.

• Install the required packages:

  pip install torch torchdiffeq pyyaml loguru tqdm matplotlib pandas

2. Configuration ⚙️

• Open the config.yaml file and adjust the model parameters and settings to match your desired scenario.
• The file is well-commented, explaining each parameter and its role in the model.

3. Running the Simulation 🧬

• Execute the main.py script:

  python main.py

• The script will:

  • Load the configuration from config.yaml.
  • Initialize the model and set the initial conditions.
  • Solve the differential equations over the specified time span.
  • Save the simulation results to a CSV file in the output directory.
  • Generate visualizations of key results and save them in the output/figures directory.
  • Log important information and progress updates to the console and a log file in the logs directory.

4. Exploring the Results 📊

• The simulation results are saved as a CSV file (simulation_results.csv) in the output directory. You can open this file in a spreadsheet program or use Pandas in Python to analyze the data.
• Visualizations of blood volume/pressure and cytokine levels are saved as PNG files in the output/figures directory. These plots provide a visual representation of the model's behavior over time.
• The log file in the logs directory contains detailed information about the simulation process, including parameter values, progress updates, and any warnings or errors encountered.