Enable automated testing for Public Dashboard

[iantei]

Currently, we are testing the changes for the Public Dashboard in the following way:

1. Load the dataset into MongoDB using load_mongodump.sh script.

2. Execute the docker-compose:

   docker-compose -f docker-compose.yml build
   docker-compose -f docker-compose.yml up

3. Once the docker containers are up, we enter into the docker container for server

   docker exec -it em-public-dashboard-notebook-server-1 /bin/bash
   source setup/activate.sh && cd saved-notebooks

4. Then execute the generate_plots.py scripts to pass notebooks as the args

   python bin/generate_plots.py generic_metrics.ipynb default
   python bin/generate_plots.py generic_metrics_sensed.ipynb default
   ...

5. Then launch the http://localhost:3274 and test the changes in it.

[iantei]

Explored into other repository, primarily, e-mission-server, to see how the testing was executed automatically.

• Use of Github Actions

Following approach has been used for the setup of automated testing:

• Starts with the .github/workflows/test_with_docker.yml
• This calls up for run: docker compose -f setup/docker-compose.tests.yml up --exit-code-from web-server
• The docker-compose.tests.yml has the following configuration:

  services:
  web-server:
  #builds from tests/dockerfile
  build: tests

• Docker file has the following:

  
  COPY start_script.sh /start_script.sh

CMD ["/bin/bash", "/start_script.sh"]

This runs: `start_script.sh` which has the following:

source setup/setup_tests.sh source setup/activate_tests.sh ./runAllTests.sh

runAllTests.sh has the following script:

PYTHONPATH=. python -m unittest discover -s emission/tests -p Test*;

Finds all the unites files which start with Test, and executes the test.

[iantei]

e-mission-server makes use of GitHub Actions and unittest - unit testing framework, to execute test cases for the e-mission-server automatically.

[iantei]

For em-public-dashboard, we have extensive use of .ipynb python Jupyter notebooks, along with few python .py files.

• We should write unit test cases too, but I am tempted to explore if unittest is the right framework to use when we have to test for Jupyter notebooks.
• As I do not have prior experience with unit testing in Python, I would need to explore and evaluate few options in details, though in higher level I came across a few other testing frameworks like testbook

[iantei]

While following the above mentioned steps in e-mission-server enables unit testing the code of em-public-dashboard; I am also considering the aspect of how can we automate this process :

Accounting both of the cases of automating the current process in more linear way, and utilizing the automation unit testing process alike in server side, I would like to propose two different tasks at a very high level to execute this task of "Enable automated testing for Public Dashboard":

• Write a script to call load_mongodump.sh, launch the docker containers, execute all the generate_plots.py from a single script and log the success/failure of each notebooks. Developers can test for different program/study configuration easily. For the cases, for example, a mode is split into two, or merged into Other as such, with the code changes, I am not sure how we would be able to test it by comparing images automatically - it might still need to be evaluated manually.
• For the evaluation of alt_text and plots for each charts (in most cases), I think we could write unit test cases which evaluates the generation of these against the expected results (I still need to explore how to generate this part more). Choosing a right testing framework which helps us work more intuitively with Jupyter notebooks would be ideal.

@shankari @JGreenlee Please let me know about your thoughts on this.