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Getting started with the ve_data_science repository

Important

This is a draft document

This is a how to guide to getting started with the ve_data_science repository

Getting the repository

The first step is to clone the repository. If you do not already have git then you will need to install it:

https://git-scm.com/downloads

Next, in your terminal, change directory to the location where you want the repository to live and then run the following command.

git clone https://github.com/ImperialCollegeLondon/ve_data_science.git

That will create a new directory called ve_data_science that contains all of the current files, all of the changes ever made to those files. It also contains the details of all of the branches containing active versions of the code that differ from the core main branch. Those changes are hidden away in the .git folder.

See the GitHub Overview for details on working with Git and GitHub.

Setting up the repository for use

However, we use a number of quality assurance tools (QA) to help manage the code files and documents within this repository. You also need to do the following to get this set up working and make the most of working with VSCode, if that is the editor tool you want to use.

  1. Install python if needed. You probably already have this since it is needed to run virtual_ecosystem!

  2. Install poetry. This is a python package manager, which we are using here to maintain a set of Python tools that are likely to be used within the project. Follow the command line instructions on the poetry installation page.

  3. In the command line, run poetry install. This will install the recommended python packages, which includes the radian front-end for R, the xarray package for handling NetCDF data and the pre-commit framework for running code quality checks on changes being committed to the repository. The poetry tool creates a new Python environment that is specific to this project.

  4. If you are using Visual Studio Code, then it needs to know which python setup to use for running Python code and for running Python code quality tools. This is done by setting the Python interpreter path to match the one that poetry just created:

    • Run poetry env list --full-path, copy the result and then either add /bin/python (on MacOS or Linux) or \Scripts\python.exe (on Windows) to the end. For example: /Users/dorme/Library/Caches/pypoetry/virtualenvs/ve-data-science-ND1juKN--py3.12/bin/python
    • In the VSCode menus, select View > Command Palette and then enter interpreter in the box to find the Python: Select Interpreter command. Click on Enter interpreter path and paste in the path from above.

  5. You now need to setup the pre-commit tool, which is used to run a standard set of checks on files when git commit is run. At the command line, enter:

    poetry run pre-commit install

    This command can take quite a long time to run - among other things, it is installing a separate version of R just to be used for file checking!

  6. If you do not have R 4.4 installed, you will now need to install it.

  7. You now need to configure VSCode to work with R. This involves changing some of the settings so that it [TBD]