Skip to content

Latest commit

 

History

History
50 lines (34 loc) · 2.74 KB

README.md

File metadata and controls

50 lines (34 loc) · 2.74 KB
Raw

🩸 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.