IDAES Examples

Table of Contents

• For Users
  • Install
  • Browse notebooks
  • Build documentation locally
  • Run tests
    • Integration tests
    • Unit tests
• For Developers
  • Install
  • Run tests
  • Build documentation
  • Preprocessing
  • Copyright headers
  • Notebook names
  • How to create an example
    • Jupyter Notebook file extensions
    • Jupyter Notebook cell tags
    • Jupyter notebook metadata

This repository contains Jupyter Notebooks (and supporting Python scripts and data) that demonstrate the capabilities of the IDAES platform.

Using the idaesx command that comes with this repository, the contained Jupyter Notebooks can be opened and run locally (idaesx gui) or built into Jupyterbook documentation (idaesx build). The standard Python test runner, pytest, can be used to test that all the notebooks execute successfully.

The rest of this README is broken into separate sections for users, to view or run examples, and for developers, who may contribute modifications or new examples to the repository.

---

For Users

Install

For now, see the For Developers -> Install section.

Browse notebooks

Use the idaesx gui command to get a simple graphical UI that lets you browse and open notebooks (in a Jupyter server) for local execution.

Build documentation locally

Run idaesx build from the repository root. For more details, see the For Developers -> Build documentation section.

Run tests

Run pytest from the repository root. For more details, see the For Developers -> Run tests section.

---

For Developers

This section is intended for people who are creating and modifying examples. The examples are primarily in Jupyter Notebooks. Python support files and data may be used to keep the notebooks focused on the interactive material.

Install

Clone the repository from Github, setup your Python environment as you usually do, then run pip with the developer requirements:

# to create a conda environment first:
# conda create -n idaes-examples python=3; conda activate idaes-examples
pip install -e .[dev]

The configuration of the installation is stored in pyproject.toml.

Run tests

There are two ways to run tests: running all the notebooks (integration tests), and testing that notebooks work without running all their code (unit tests).

Integration tests

Run integration tests from the top-level (root) directory of the repository. In the root directory, tests are configured by pyproject.toml; see the tool.pytest.ini_options section.

# from the root directory of the repository
pytest

If you want to exclude certain notebooks from the integration tests, see the Preprocessing -> Jupyter notebook metadata section.

Unit tests

Run unit tests from the idaes_examples directory of the repository. In the idaes_examples directory, tests are configured by idaes_examples/pytest.ini. To run the unit tests change to do the idaes_examples directory, then run the same command:

cd idaes_examples
pytest

Build documentation

The documentation is built using Jupyterbook. We have written a thin layer around the Jupyterbook executable, which is installed as a command-line program idaesx. To build the documentation, run:

idaesx build

The output will be in idaes_examples/nb/_build/html. As a convenience, you can open that HTML file with the command idaesx view.

Preprocessing

The commands to run tests and build documentation both run a preprocessing step that creates separate copies of the Jupyter notebooks that are used for tests, tutorial exercise and solution, and documentation (see Notebook Names). These generated files should not be added to the repository. If you want to run that preprocessing step separately, use idaesx pre. To remove pre-processed files, run idaesx clean.

A diagram of how preprocessing relates to notebook usage is shown below:

 ┌───────────────────┐
 │                   │
 │ example_src.ipynb │
 │                   │
 └──────┬────────────┘
        │
        ▼            ┌──────────────────┐     ┌──────┐
 ┌────────────┐  ┌─► │example_test.ipynb├────►│pytest│
 │ preprocess ├──┤   └──────────────────┘     └──────┘
 └──────────┬─┘  │
            │    │   ┌─────────────────┐      ┌───────────┐
            │    └─► │example_doc.ipynb├─────►│jupyterbook│
            │        └─────────────────┘      └───────────┘
            │
            │        ┌─────────────────┐     ┌───────────┐
           ┌┴─┐  ┌──►│example_usr.ipynb├────►│ browse/run│
           │OR├──┤   └─────────────────┘     └───────────┘
           └──┘  │                                ▲
                 │   ┌──────────────────────┐     │
                 │   │example_exercise.ipynb├─────┘
                 └──►│example_solution.ipynb│
                     └──────────────────────┘

Notebook names

Notebooks all have a file name that fits the pattern notebook-name_ext.ipynb*. When creating or modifying notebooks, you should always use ext = src. Other extensions are automatically generated when running tests, building the documentation, and manually running the preprocessing step. See the table of notebook types for details.

How to create an example

See tutorial.md in this directory for a more detailed step-by-step tutorial.

There are two main steps to creating a new notebook example.

1. Add Jupyter Notebook and supporting files
   1. If this is a new category of notebooks, create a directory. The directory name should be in lowercase with underscores between words. For example: 'machine_learning'.
   2. Notebook filename should be in lowercase with underscores and must end with '_src.ipynb'. For example: 'my_example_src.ipynb'.
   3. Add -- in the same directory as the notebook -- any data files, images, or Python files needed for it to run.
2. Add Jupyter notebook to the Jupyterbook table of contents in idaes_examples/nb/_toc.yml.
   1. The notebook will be a section. If you added a new directory, you will create a new chapter, otherwise it will go under an existing one. See Jupyterbook documentation for more details.
   2. Refer to the notebook as 'path/to/notebook-name_doc' (replace '_src' with '_doc' and drop the '.ipynb' extension). For example: 'machine_learning/my_example_doc'.
   3. If you created a new directory for this notebook, make sure you add an index.md file to it. See other index.md files for the expected format.

You should test the new notebook and build it locally before pushing the new file, i.e., run pytest and idaesx build.

Jupyter Notebook file extensions

Each source Jupyter notebook (ending in '_src.ipynb') is preprocessed to create additional notebooks which are a copy of the original with some cells (steps in the notebook execution) removed.

Notebook type	Description	Ends with
source	Notebook source	_src.ipynb
testing	Run for testing	_test.ipynb
exercise	Tutorial exercises only	_exercise.ipynb
solution	Tutorial ex. and solutions	_solution.ipynb
documentation	Show in documentation	_doc.ipynb
user	Run by end-users	_usr.ipynb

Jupyter Notebook cell tags

Preprocessing uses the feature of Jupyter notebook cell tags to understand which additional notebooks to create.

The following tags are understood by the preprocessing step:

• testing: Remove this cell, except in the testing notebooks
• exercise: The presence of this tag means a notebook is a tutorial. Generate an exercise and solution notebook, and keep this cell in both. Remove this cell in the documentation notebook.
• solution: The presence of this tag means a notebook is a tutorial. Generate an exercise and solution notebook, but remove this cell in the solution notebook; keep the cell in the documentation notebook.
• noauto: This tag means that this cell should not be run during automated notebook execution. The cell will be removed in testing and documentation notebooks.
• auto: This tag means that this cell should only be run during automated notebook execution. The cell will be removed in all notebooks except testing and documentation.

All other tags, including the standard Jupyterbook tags for hiding or removing cells, will be ignored.

Jupyter notebook metadata

In addition to per-cell tags, the preprocessor also can look at notebook-level metadata. This is currently used for only one purpose: to tell the preprocessor not to generate a 'test' notebook, and thereby to skip the given notebook in the tests. In order to make this happen, either manually edit the notebook source or use the Jupyter notebook "Edit -> Edit Notebook Metadata" menu item to add the following section to the notebook-level metadata:

"idaes": {
   "skip": ["test"]
}

Copyright headers

You can add (and update) copyright headers to Python files and Jupyter Notebooks using the addheader command with the included addheader.yml configuration file:

# first see what would be changed (using -n)
addheader -n -c addheader.yml
# if this is OK, add the headers
addheader -c addheader.yml

All existing notebooks and Python files will be automatically discovered and modified as needed.