Running GME notebooks

For the Jupyter/IPython notebooks to run successfully, the environment variable PYTHONPATH must include paths to the parent directories of both the GME and GMPLib Python packages. On MacOS and Linux platforms this is achieved by modifying the start-up script of your preferred shell (see notes on installation and set-up) and by launching the Jupyter notebook server from this shell and accessing the server in a browser. The chosen GME notebook can then be opened and executed inline.

This approach is fine for single-shot runs of GME with a particular choice of parameters. However, if we want to run a group of analyses with several choices of parameter sets, inline execution is inconvenient: bulk, offline execution is much more efficient. Shell scripts (written in bash) are provided to make this possible.

For example, the TimeInvariant.ipynb notebook can be loaded into a Jupyter notebook server, modified to work with a particular choice of JSON parameter file selected from here, and run inline in a browser. On the other hand, if we wish to run this notebook several times with against a set of parameter files, we can do the following:

• specify the list of JSON parameter files in TimeInvariant_jobs.py

• run the batch shell script run_jobs.sh from the notebook directory using the shell command ../run_jobs.sh TimeInvariant_jobs (with the path to the script modified as appropriate)

The script steps through the list of parameter files and sets the following environment variables

• GME_NB_PR, short for “GME notebook parameter files” = Python list of parameter filename strings

• GME_WORKING_PATH = the absolute path to the GME package (its root directory)

before invoking the Jupyter interpreter with nbconvert as follows:

jupyter nbconvert --to notebook --execute $nb_filename \
        --log-level=40 --ExecutePreprocessor.timeout=-1 --clear-output

See here and here for more information on this technique.

The run_jobs.sh script as written runs the notebooks in-place, overwriting them each time execution completes. If you wish to record each executed notebook elsewhere, modify the invocation above to something like

jupyter nbconvert --to notebook --execute $nb_filename --output $job_nb_filename \
        --log-level=40 --ExecutePreprocessor.timeout=-1 --clear-output

and in the loop reassign $job_nb_filename for each job.