Minimal-Viable-Package

Demonstrate the minimal concepts/training and recommendations to structure a project on Git, build Python packages, unit testing, GitHub Actions, Read the Docs (RTD), package managers, distributions, containers, and high performance computing (HPC).

We intend the README tutorial suggestions to be read and followed linearly.

Project Structure

A minimal example of a complete Python package's working directory, will look like something below. In this README tutorial, we'll review how to build this as hassle free (as we're aware of).

minimal-viable-package # The working directory
├── README.md
├── data # A data directory for any relevant data used for unit testing your package, training a model, etc.
│   └── the-zen-of-python.txt
├── docs  # This makes the module's ReadTheDocs (RTD).
│   ├── Makefile
│   ├── build
│   ├── make.bat
│   └── source
│       ├── _static
│       ├── _templates
│       ├── conf.py # RTD configuration file
        # The reStructuredText (RST) file format is for textual data used in Python programming for documention.
│       ├── example.rst # The Python package's module and submodule API documentation. The file is named after the module.
│       ├── index.rst
│       └── modules.rst
├── env # The environment directory for sharing and reproducing the Python package or model. 
│   └── mvp_env.yml
├── pyproject.toml # Definition of the build process for the package.
├── src # The src directory contains all of the source material for building the project.
│   └── example # The Python example package directory.
│       ├── __init__.py # This makes the directory a package.
│       └── welcome.py # The example's welcome module.
└── test # The test directory contains all of the unit testing material.
    └── test_greeting.py # A unit test for the example.welcome submodule greeting

You might notice a few files are missing, like the ones that start with a period. These are considered hidden folders in Unix-like operating systems, and in this README tutorial, we'll review the directory .github and the files .gitignore and .readthedocs.yaml.

For more references on project directory structure/organization for building a Python package, here are a few helpful links:

A Practical Guide to Setuptools and Pyproject.toml

Integrated Development Environment (IDE)

This repository was built in Visual Studio Code, using the autoDocstring extension for generating our python docstrings automatically.

Building a Python Package

We can automate the majority of building a Python package, however, there are a few starting steps to consider. Here we'll outline the best practices (that again we're aware of) to simplify the process.

In this section of the README tutorial, we're going to review the minimal steps to create this repository from scratch.

1. Create a Git repository for the Python package.

$ mkdir minimal-viable-package
$ cd minimal-viable-package
$ git init
$ touch README.md
$ echo "# Minimal-Viable-Package" > README.md
$ git add README.md
$ git commit -m "Initial commit with README"
$ git push

2. Install conda

3. Create a clean conda environment and then activate.

$ conda create --name mvp_env python=3.9
$ conda activate mvp_env

Note: The mvp_env is shorthand for our minimal-viable-package environment.

4. Install the minimal depedencies for unit testing and building our documentation.

Here's how we would install each dependency individually:

(mvp_env)$ conda install -c conda-forge flit
(mvp_env)$ conda install -c conda-forge tree
(mvp_env)$ conda install -c anaconda sphinx
(mvp_env)$ conda install -c conda-forge sphinx_rtd_theme

Now that we have a minimal environment for our package, let's export the dependencies to a yet another markdown language or yml file, so we don't have to do this again.

(mvp_env)$ cd minimal-viable-package
(mvp_env)$ mkdir env # Create an (env)ironment directory for our package to help make it more reproducible
(mvp_env)$ cd env
(mvp_env)$ conda env export -n mvp_env > mvp_env.yml

For example, if you cloned this repository and had conda installed, here's how you would create the environment.

$ git clone https://github.com/bmi203-2023/minimal-viable-package.git
$ cd env
$ conda env create -f environment.yml
$ conda activate mvp_env
$ conda deactivate mvp_env # how to deactivate the conda environment

Note: Keeping the mvp_env active is not necessary for this tutorial's entire build, documentation, and test cycle. We will specify when to activate the environment. You can leave the environment activated unless you're installing/testing/developing other Python libraries. Otherwise, we suggest deactivating the environment.

5. Create a pyproject.toml in the working directory that specifies the package's build system. Let's also add a .gitignore to ignore files we don't want to push.

Using the terminal or your favorite IDE, please follow these steps:

$ cd minimal-viable-package
$ touch pyproject.toml
$ vim pyproject.toml

Copy/Paste and update the relevant fields with <your info>.

[build-system]
requires = [
	"flit_core >=3.2,<4",
	"python_version >= '3.9'"
	]
build-backend = "flit_core.buildapi"

[project]
name = "<the project's module name>" # example
authors = [{name = "<name>", email = "<email>"}]
license = {file = "LICENSE"}
classifiers = ["License :: OSI Approved :: MIT License"]
dynamic = ["version", "description"]
dependencies = ["pytest", "numpy", "scipy", "matplotlib", "scikit-learn", "sphinx"]

[tool.coverage.run]
source = ["src"] # parent directory of package

[project.urls]
Home = "https://github.com/<your git handle or organization>/<repository name>"

Next let's create .gitignore in our working directory to ignore non-essential files when committing/pushing our changes.

$ cd minimal-viable-project
$ touch .gitignore

.ipynb_checkpoints/ # jupyter notebook save points
src/example/__pycache__ # build cache for our Python package
build/*
.DS_Store # Desktop services storre, an invisible file on the macOS that's automatically created

6. Create a source directory for your package and modules.

$ mkdir src
$ cd src
$ mkdir example
$ cd example
$ touch __init__.py
$ touch welcome.py

7. Iteratively document/develop your package and modules. See this repository's simple example here.

If you're using the Visual Studio Code IDE and the autoDocstring extension, we can automate our docstrings and use type hints to improve readability.

For example, in the submodule example.welcome.Greeting() we have a class method called the_zen_of_python that reads a text file and returns each line of the file's message as an element in a list of strings. We want to parameterize the class method to read the file from any location and return the list to read. To do this we'll set a parameter of type string for a user to indicate the file path.

Using type hints, we can indicate in the class method definition the type of input parameter (str) and the output type (->) a list of strings list[str]:

def the_zen_of_python(self, read_file: str) -> list[str]:

Now autodocstring can automate the docstring for us to fill in the descriptions. We can enable this by hitting enter after our class method definition, triple quotes, and cmd+shift+2 (for macs).

def the_zen_of_python(self, read_file: str) -> list[str]:
    """_summary_

    Args:
        read_file (str): _description_

    Returns:
        list[str]: _description_
    """

Our completed docstring that Read the Docs can use to automate our Python package's documenation will look like this:

def the_zen_of_python(self, read_file: str) -> list[str]:
  """Reads a text file containing The Zen of Python and returns a list where each element is one line of the 19 aphorisms.

  Args:
      read_file (str): Path to the txt file containing The Zen of Python.      

  Returns:
      list[str]: List of strings, where each element is one line of The Zen of Python.
  """
  with open(read_file) as f:
      zen_list = [line.strip() for line in f.readlines()]
  return zen_list

After we've documented our package, we can begin testing the build of our Python package.

$ cd minimal-viable-package
$ conda activate mvp_env
(mvp_env)$ flit install -s # builds your package in editable or development mode

8. As you iteratively develop each module and submodule, consider your edge cases and design rationally explained unit tests to assess them. We suggest naming your unit tests by their associated module and their test functions as the submodule you're evaluating.

$ cd minimal-viable-package
$ mkdir test
$ touch test_greeting.py # name the modules/submodule you're evaluating

In the next section, we'll review our unit test suggestions. At this point your working directory should look like this:

minimal-viable-package # The working directory
├── README.md
├── data # A data directory for any relevant data used for unit testing your package, training a model, etc.
│   └── the-zen-of-python.txt
├── env # The environment directory for sharing and reproducing the Python package or model. 
│   └── mvp_env.yml
├── pyproject.toml # Definition of the build process for the package.
├── src # The src directory contains all of the source material for building the project.
│   └── example # The Python example package directory.
│       ├── __init__.py # This makes the directory a package.
│       └── welcome.py # The example's welcome module.
└── test # The test directory contains all of the unit testing material.
    └── test_greeting.py # A unit test for the example.welcome submodule greeting

Assuming you made and populated the data folder.

To create a project directory tree as seen above for your README, please follow these commands:

$ cd minimal-viable-package
$ tree minimal-viable-package -o minimal-viable-package/tree.md

For this tutorial, we copied/pasted the contents of tree.md into this README and edited accordingly.

For more references on Python packaging, here are a few helpful links:

Setting up a repository
A pyproject.toml Developer's Cheat Sheet

Unit Testing

In our minimal-viable-package unit test submodule example (e.g., the Greeting class in welcome.py or welcome.Greeting()), we evaluate class attributes and methods based on our set parameters and expectations. Please find the unit test here.

For more references on unit testing using pytest, here are a few helpful links:

pytest: helps you write better programs

GitHub Actions

Now that all of our configuration and recipe files have been made, we can automate our build and testing.

1. Create a .github/workflows directory tree.

$ cd minimal-viable-package
$ mkdir .github
$ cd .github
$ mkdir workflows

2. Create YML file and specify the commands to run whenever your code is pushed to the repository (i.e, unit tests, RTD, etc.).

$ cd minimal-viable-package/.github/workflows
$ touch main.yml
$ vim main.yml

Copy/Paste and update the relevant fields with <your info>.

# This is a basic workflow to help you get started with Actions
name: <your package's name>

# Controls when the workflow will run
on:
  # Triggers the workflow on push or pull request events but only for the main branch
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

  # Allows you to run this workflow manually from the Actions tab
  workflow_dispatch:

# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
  # This workflow contains a single job called "build"
  build:
    # The type of runner that the job will run on
    runs-on: ubuntu-latest

    # Steps represent a sequence of tasks that will be executed as part of the job
    steps:
      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
      - uses: actions/checkout@v2
      
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install .
      
      - name: Run unit tests
        run: python -m pytest -v test/*

3. Add a workflow status badge to your README to indicate if you're failing or passing.

$ cd minimal-viable-package
$ vim REAMD.md

Copy/Paste and update the relevant fields with <your info>.

![BuildStatus](https://github.com/<your githandle>/<your repository name>/actions/workflows/main.yml/badge.svg?event=push)

4. Push your commits and then check if your workflow badge indicates passing.

You've now specified the minimal requirements to automate the build, documentation, and test cycle for your software package. Congrats!

For more references related to GitHub Actions, here are a few helpful links:

Understanding GitHub Actions
Adding a workflow status badge

Read the Docs

Read the Docs (RTD) is a software documentation service for automating the building, version control, and hosting of your software's documentation. In this section of the README tutorial, we'll review how we built the RTD for the minimal-viable-package's example module.

1. Create a RTD account and link your GitHub account.

ReadtheDocs

2. Create the RTD directory for your Python package.

Update the relevant fields with <your info>.

$ cd minimal-viable-example
$ conda activate mvp_env
(mvp_env)$ mkdir docs
(mvp_env)$ cd docs
(mvp_env)$ sphinx-quickstart

Welcome to the Sphinx 4.4.0 quickstart utility.

Please enter values for the following settings (just press Enter to
accept a default value, if one is given in brackets).

Selected root path: .

You have two options for placing the build directory for Sphinx output.
Either, you use a directory "_build" within the root path, or you separate
"source" and "build" directories within the root path.
> Separate source and build directories (y/n) [n]: y

The project name will occur in several places in the built documentation.
> Project name: <Minimal Viable Project>
> Author name(s): <Andrew Blair>
> Project release []: <0.1.0>

If the documents are to be written in a language other than English,
you can select a language here by its language code. Sphinx will then
translate text that it generates into that language.

For a list of supported codes, see
https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-language.
> Project language [en]: 

Creating file (RTD will automate the full path to) minimal-viable-package/docs/source/conf.py.
Creating file (RTD will automate the full path to) minimal-viable-package/docs/source/index.rst.
Creating file (RTD will automate the full path to) minimal-viable-package/docs/Makefile.
Creating file (RTD will automate the full path to) minimal-viable-package/docs/make.bat.

Finished: An initial directory structure has been created.

You should now populate your master file  (RTD will automate the full path to) minimal-viable-package/docs/source/index.rst and create other documentation
source files. Use the Makefile to build the docs, like so:
   make builder
where "builder" is one of the supported builders, e.g. html, latex or linkcheck.

Let's review the most important folder/files RTD autogenerated for us in the docs directory:

docs
├── Makefile
├── build
├── make.bat
└── source # The directory where all our .rst files and configuration files will reside.
    ├── _static
    ├── _templates 
    ├── conf.py # The configuration file where all Sphinx settings are specified and ran to extract/build our desired configuration.
    ├── index.rst # The file that tells Sphinx how to render our front index page.

Before we build our documentation, we need to update conf.py.

3. Update the theme, autodoc configuration settings, and sphinx.ext.autodoc references in conf.py.

$ cd minimal-viable-package/docs/source
$ vim conf.py

In conf.py, modify the following configuration variable:

html_theme = 'sphinx_rtd_theme'

Next, if we want Sphinx to autogenerate our package's documentation from our code using the autodoc extension, we need to point Sphinx to the directory containing our Python package's source code. We'll do this by adding the following lines of code to the top of conf.py.

import os
import sys

sys.path.insert(0, os.path.abspath('../../src/example'))

In addition, we need to specify the auto directive extensions for every object we want to document in conf.py.

# -- General configuration
extensions = ['sphinx.ext.todo', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc']

For a more detailed view, please see the minimal-viable-package's conf.py link here.

Now that we've set our RTD configuration file to our specifications, we need to instruct the RTD build process to incoporate our package's docstrings into the documentation. In the next step, we'll review how to use autodoc to build the package's rst files for automated documentation.

4. Auto-generate documentation from docstrings in your Python package's source files.

The following commands can be used to auto-generate .rst files for our Python module and submodules.

$ cd minimal-viable-package/docs/
$ conda activate mvp_env
(mvp_env)$ sphinx-apidoc -f -o source/ ../src/example
Creating file ./source/example.rst.
Creating file ./source/modules.rst.

Since we only have one module (i.e., example), let's focus on the example.rst file.

example package
===============

Submodules
----------

example.welcome module
----------------------

.. automodule:: example.welcome
   :members:
   :undoc-members:
   :show-inheritance:

Module contents
---------------

.. automodule:: example
   :members:
   :undoc-members:
   :show-inheritance:

Directing attention to the Submodules section, we should see the directive options: :members, :undoc-members, and :show-inheritance. If these are generated, autodoc should be able to generate documentation from our docstrings.

Since our documentation requires the minimal-viable-package's example module to be installed, we'll next review how to specify an RTD configuration file to build and install the package.

5. Create a yml file for the RTD build in the working directory.

Similar to our earlier sections on pyproject.toml, the conda environment yml file, unit tests, and GitHub Actions we need to specify the recipe steps for RTD to build and document our package.

$ cd minimal-viable-project
$ touch .readthedocs.yaml

Copy/paste the the snippet below into .readthedocs.yaml.

# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details

# Required
version: 2

# Set the version of Python and other tools you might need
build:
  os: ubuntu-22.04
  tools:
    python: "3.11"

python:
  install:
    - method: pip # pip will recognize the pyproject.toml for installation
      path: .

Note: For the minimal-viable-package, we didn't need to specify any user specific information for the build, but this might not be true for more complex projects.

We now have everything in place to create an RTD for our Python package. Let's add, commit, and push to Git, then navigate to RTD.

6. Build and publish your package documentation to ReadTheDocs by manually connecting to Git.

Once you're signed into RTD, there are a few different options to import your project. For this tutorial, we linked our GitHub to the ReadTheDocs accounts. If you take this route, you can import your GitHub repository in the RTD dashboard and then import your specific project.

Next, navigate to your project's GitHub repository and selecting the Settings page tab. In the left hand corner of the Settings, select Webhooks and then Add webhook.
For Payload URL, use the URL of the integration on your Read the Docs project, found on the RTD project’s Admin > Integrations page.
You may need to prepend https:// to the RTD URL. For Content type, both application/json and application/x-www-form-urlencoded work. Leave the Secrets field blank.
Select Let me select individual events, and mark Branch or tag creation, Branch or tag deletion, Pull requests and Pushes events
Ensure Active is enabled; it is by default
Finish by clicking Add webhook. You may be prompted to enter your GitHub password to confirm your action.

Note: RTD defaults to checking out the project's "master" branch. However, Git will default your initial repository's name to "main." You can rename your primary Git branch to "master," as we did in this example. Or, in your project's RTD, you can navigate to the "Admin" tab and then "Advanced Settings", where you can set the default branch RTD checks outs.

If the documentation passes, let's next add a badge to indicate you passed.

7. Add the doc badge to the top of the README.

$ cd minimal-viable-package
$ vim README.md

Copy/Paste and update the relevant fields with <your info>.

[![Documentation Status](https://readthedocs.org/projects/<Project Slug>/badge/?version=latest)](https://<Project URL>.readthedocs.io/en/latest/?badge=latest)

Congratulations, and thank you for making it this far in the tutorial! The next sections will review our best practices for development operations and high performance computing.

For more references related to Sphinx-RTD-Tutorial, here are a few helpful links:

Read the Docs Tutorial
Sphinx-RTD-Tutorial
Sphinx Theme
Sphinx.ext.autodoc
Manually Connect Git & RTD

Package Managers, Distributions, & Containers

Miniconda
Homebrew
Docker
Singularity

How to install a GitHub Python package without a requirements.txt file?

$ pip install pipreqs
$ pipreqs /path/to/project

Stack Overflow: Automatically create requirements.txt GitHub: pipreqs

How to install a python package using pip and a requirements.txt file?

$ pip install -r requirements.txt

How to install Python packages with pip and requirements.txt

UCSF High Performance Computing (HPC) Resources

TODO

[] Package Managers, Distributions, & Containers
[] UCSF HPC
[] RTD for multiple modules
[] RTD with Plotly integration
[] RTD with machine learning API documentation

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README.md

README.md

Minimal-Viable-Package

Project Structure

Integrated Development Environment (IDE)

Building a Python Package

Unit Testing

GitHub Actions

Read the Docs

Package Managers, Distributions, & Containers

How to install a GitHub Python package without a requirements.txt file?

How to install a python package using pip and a requirements.txt file?

UCSF High Performance Computing (HPC) Resources

TODO

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Minimal-Viable-Package

Project Structure

Integrated Development Environment (IDE)

Building a Python Package

Unit Testing

GitHub Actions

Read the Docs

Package Managers, Distributions, & Containers

How to install a GitHub Python package without a requirements.txt file?

How to install a python package using pip and a requirements.txt file?

UCSF High Performance Computing (HPC) Resources

TODO