• Update conda

  conda update --all
  

• Create a conda environment and run the install script:

  conda create -y --name bgc_md2 python=3
  conda activate bgc_md2
  ./install_developer_conda.sh 

  This will install the dependencies and run python setup.py develop for every subpackage so that your code changes in one of these packages take mmediate effect.

* Run the tests.
  ```
  cd tests
  ./run_tests.py
  ```
  If you can run this script successfully, you have a working installation of ComputabilityGraphs and can run all functions. 

• Troubleshooting:

  • We noticed that in MacOS, it is necessary to update packages in the conda environment before running the tests successfully. Try to update conda ( conda update --all) and run the tests again.

• Working with the installation:

  • pulling: Since you will nearly always pull with the --recurse-submodules flag
    consider creating an alias

    git config alias.spull 'pull --recurse-submodules'
    

    which enables you to say git spull to achieve the same effect

  • Tips to work with git submodules:

• The latest build of the package documentation can be found here:.

1. Investigations of a single model (or modelrun).

We try to keep the master green and develop new features or bug fixes in short lived branches that are then merged back into the master https://nvie.com/posts/a-successful-git-branching-model/ See also https://git-scm.com/book/en/v2/Git-Branching-Branches-in-a-Nutshell https://git-scm.com/book/en/v2/Git-Tools-Submodules for the work on the dependencies.

• Example workflow to work on a feature branch iss26-non-importable-models you are asked to review
  • git branch -a (shows all branches including remotes)
  • git checkout --track origin/iss26-non-importable-models (creates a local copy that you can test)
• Example to create your own feature branch (here to fix an issue )
  • git checkout -b iss53

• The 'Computers' and 'MVars' represent a set of types and strictly typed functions (including the return values). This has been implemented with the new python type annotations.
  An advantage is that we can express our idea in a well defined and well documented way and avoid extra effort for the user..
• The computibility graph is expensive to create and only changes if new Computers and MVars are created. It should be cached, which encurages the use of immutable data structures. (since we can use functools )