Jupyter Notebooks

Interactive notebooks for hands-on exploration of Argus functionality.

Available Notebooks

Data Exploration

• 01.explore_how_minnow_works.ipynb - Understanding the Minnow framework
• 02.run_on_mock_data_challenge.ipynb - IPTA mock data analysis
• 02e.loading_mock_data_with_argus.ipynb - Data loading with Argus

Parameter Estimation

• 09.estimate_psr_noise_params.ipynb - Pulsar noise characterization
• 10.estimate_timing_ephemeris_params.ipynb - Timing model parameters
• 11.inspect_parameter_estimation_results.ipynb - Result analysis

Analysis Methods

• 07.explore_inference_convergence.ipynb - Convergence diagnostics
• 08.explore_different_likelihood_values.ipynb - Likelihood evaluation
• 12.plot_likelihood_curves.ipynb - Visualization techniques

Signal Processing

• 05.PSD_for_OU_process.ipynb - Ornstein-Uhlenbeck processes
• 06.PSD_for_OU_GW_process.ipynb - Gravitational wave PSDs

Running the Notebooks

To run these notebooks locally:

# Clone the repository
git clone https://github.com/ADACS-Australia/tkimpson_2025a.git
cd tkimpson_2025a

# Install dependencies
poetry install

# Start Jupyter
jupyter lab notebooks/

Data Requirements

Some notebooks require large datasets that are not included in the repository. Download instructions are provided within each notebook.

Cloud Computing

These notebooks can be run on cloud platforms like Google Colab or Binder for easier access.