Installation and Usage

To reproduce the analysis in these notebooks, follow the steps below:

  1. Clone the Repository

    Clone the repository to your local machine:

    git clone https://gitlab.developers.cam.ac.uk/phy/data-intensive-science-mphil/assessments/a4_coursework/jlt67.git
cd jlt67

  2. Create a Fresh Virtual Environment

    It is recommended to use a clean virtual environment to avoid dependency conflicts:

    python -m venv env
source env/bin/activate   # For macOS/Linux
env\Scripts\activate      # For Windows

  3. Install Dependencies

    1. Install Python dependencies from requirements.txt:

      pip install -r requirements.txt

    2. Additional Installation: Extreme Deconvolution

      This project utilises the extreme_deconvolution algorithm for Gaussian Mixture Model (GMM) fitting, as described in Bovy et al. (2011). Note that this package is not available on PyPI and must be installed manually.

      Source Repository: https://github.com/jobovy/extreme-deconvolution

      Follow these steps to install:

      1. Clone the Repository

        git clone https://github.com/jobovy/extreme-deconvolution.git
cd extreme-deconvolution

      2. Build the C Library

        make

      3. Build the Python Wrapper

        make pywrapper

      4. Add the Python Wrapper to Your Environment

        Add the py/ subdirectory to your PYTHONPATH so it can be imported in your Python environment. You can do this by appending the following line to your shell configuration (e.g., ~/.bashrc, ~/.zshrc):

        export PYTHONPATH=$PYTHONPATH:/jlt67/extreme-deconvolution/py

      For more detailed instructions, see the project’s README on GitHub.

  4. Set Up a Jupyter Notebook Kernel

    Register the virtual environment so it’s available in Jupyter:

    python -m ipykernel install --user --name=env --display-name "Python (Chemo-Dynamical GMM)"

  5. Run the Notebooks

    Launch Jupyter and select the kernel named Python (Chemo-Dynamical GMM) when executing the notebooks.make html