Quick Start
Prerequisites
Before you begin, ensure you have the following installed on your system:
- Python 3.8+
- Conda (Recommended for managing physics-specific dependencies)
- Git
1. Clone the Repository
Start by cloning the bootcamp materials to your local machine:
git clone https://github.com/autorelorg/GWData-Bootcamp.git
cd GWData-Bootcamp
2. Set Up Your Environment
Gravitational wave analysis requires specialized libraries. We recommend using a Conda environment to handle the installation of lalsuite and pytorch.
# Create and activate a new environment
conda create -n gw-bootcamp python=3.9
conda activate gw-bootcamp
# Install physics-specific libraries (LALSuite)
conda install -c conda-forge lalsuite
# Install Deep Learning and Data Science stack
pip install torch numpy matplotlib scipy ipython
3. Access the First Lesson
The bootcamp is structured into modules covering Python basics, Machine Learning, and Deep Learning. You can start by exploring the introductory materials or viewing the pre-rendered HTML lessons.
Option A: Explore via Browser
Open the introductory session recordings and documentation:
- Introductory Video: Bilibili - Part Zero
- Lesson 0 Documentation: Navigate to
2023/intro/readme.mdfor the course roadmap and learning methodology.
Option B: Run the Python Baseline
To verify your installation, navigate to the Deep Learning baseline folder and ensure the scripts can initialize:
cd 2023/deep_learning/baseline/
python main.py
Note: This script initializes the DatasetGenerator and MyNet architecture. It will check for GPU availability automatically via torch.cuda.is_available().
4. Working with Notebooks
Most lessons are provided as interactive notebooks. While the repository contains .html exports for quick reading (e.g., sklearn_model_ensemble.html), you can run the source code by opening the corresponding .ipynb files in Jupyter:
pip install jupyterlab
jupyter lab
Next Steps
Once your environment is ready, proceed to Part Zero to understand the knowledge architecture of gravitational wave data analysis and the specific project requirements for this bootcamp.