readme.md

OpenMDAO Book

This repo contains the prototype for new, more interactive documentation of OpenMDAO based on jupyter-book.

For now I've left in the default Jupyter-book files, which includes some useful how-to information. OpenMDAO-specific documentation begins with main.md.

Pre-requisites

You will need to pip install jupyter-book. Also, building the notebook seems to require Python 3.8.

Building

From the repo root simply run the command below to build the docs from the root directory:

python build_jupyter_book.py

Notebook Guidelines

1. Each notebook should include this block at the top to import OpenMDAO if not already available. This is necessary on the cloude-based notebook environments like colab.

try:
    import openmdao.api as om
except ImportError:
    !python -m pip install openmdao[notebooks]
    import openmdao.api as om

This cell should be tagged with the following metadata. The "remove-input" and "remove-output" tags prevent it from showing up in the documentation, and the "hide_input" portion collapses the input cell. To add a tag in Jupyter select View -> Cell Toolbar -> Tags add add any of the tags desired.

{
  "hide_input": true,
  "tags": [
    "remove-input",
    "remove-output",
    "active-ipynb"
  ],
  "trusted": true
}

2. Executed code in notebooks should be tested using the same assertions used in unittests.

For instance, in the paraboloid case we have:

# This code block is hidden by default.
# It exists to verify that the above code works correctly.

from openmdao.utils.assert_utils import assert_near_equal

# minimum value
assert_near_equal(prob.get_val('paraboloid.f'), -27.33333, 1e-6);

# location of the minimum
assert_near_equal(prob.get_val('paraboloid.x'), 6.6667, 1e-4);
assert_near_equal(prob.get_val('paraboloid.y'), -7.33333, 1e-4);

It's not necessary to show this in the documentation, so remove it using the same metadata tags above to hide.

3. We will have Github actions to check for things like notebooks with output, missing import headers, and more.

4. Since 'n2.html' files and other build artifacts need to be manually copied over to the output _build directory to make the docs, each example notebook should be kept in its own directory.

Troubleshooting

• If you get an error jupyter_client.kernelspec.NoSuchKernel: No such kernel named name-of-your-env, this means your notebook's kernel is not set to Python 3. To fix this, select Kernel -> Change Kernel -> Python 3 and then rebuild the docs.