06_documentation.qmd

---
title: Documentation
format: 
    html: default
    revealjs:
        output-file: 06_documentation_slides.html
        slide-number: true
footer: Python package development
logo: academy_logo.png
---


## Why document your code?

![](https://imgs.xkcd.com/comics/manuals.png)

::: {.incremental}

* Make it easier for others to use your code
* Make it easier for **you** to use your code

:::

## Readme.md

* A readme file is a text file that introduces and explains a project.
* Always include a readme file in your project.
* You can put readme files in any directory, and you can have more than one in a single project.

## Requirements

* Mention the requirements for your package
    - Operating system
    - Python version
    - Other non-Python dependencies, e.g. VC++ redistributables
* Include information on how to install your package
    - `pip install my_package`
    - `pip install https://github.com/DHI/{repo}/archive/main.zip`

## Notebooks

* Jupyter notebooks are a great way to document your code
* Good for prototyping
* In a later stage, notebooks can be used to demonstrate how to use your code
* Not a replacement for documentation for a professional package


## Docstrings

  ```{.python code-line-numbers="|1|4-18"}
"""K-means clustering."""

class KMeans(_BaseKMeans):
    """K-Means clustering.
    
    Parameters
    ----------
    n_clusters : int, default=8
        The number of clusters to form as well as the number of
        centroids to generate.

    Examples
    --------
    >>> X = np.array([[1, 2], [1, 4], [1, 0],
    ...               [10, 2], [10, 4], [10, 0]])
    >>> kmeans = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(X)
    >>> kmeans.labels_
    array([1, 1, 1, 0, 0, 0], dtype=int32)
```
[sklearn.KMeans](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/cluster/_kmeans.py)

---

```python
>>> from sklearn.cluster import KMeans
>>> help(KMeans)
class KMeans(_BaseKMeans)
 |  KMeans(n_clusters=8, *, init='k-means++', n_init='warn')
 |
 |  K-Means clustering.
 |
 |  Parameters
 |  ----------
 |  n_clusters : int, default=8
```
. . .
![](images/vs_code_docstring.png)

---

![](images/api_docs.png)

::: {.notes}
Write once, read anywhere!
:::


## Docstring - Numpy format {.smaller}



```python
def function_name(param1, param2, param3):
    """Short summary.
    
    Long description.
    
    Parameters
    ----------
    param1 : int
        Description of `param1`.
    param2 : str
        Description of `param2`.
    param3 : list of str
        Description of `param3`.
    
    Returns
    -------
    bool
        Description of return value.
    """
    pass
```

::: {.notes}
There are several docstring formats. The most common is the numpy format, used by scikit-learn, pandas, numpy, scipy, etc. 
:::

## Type hints {.smaller}

From Python 3.6, type hints can be used in addition to the type in the docstring.

```{.python code-line-numbers="|1"}
def remove_outlier(data:pd.DataFrame, column:str, threshold:float=3) -> pd.DataFrame:
    """Remove outliers from a dataframe.
    
    Parameters
    ----------
    data : pd.DataFrame
        Dataframe to remove outliers from.
    column : str
        Column to remove outliers from.
    threshold : float, optional
        Number of standard deviations to use as threshold, by default 3
    
```


## doctest

Using code without documentation is hard, but using code with wrong documentation is even harder. 

How can you make sure that the documentation is correct? 

. . .

The answer is the `doctest` module built in to the Python standard library. 

. . .  

::: {.callout-tip}

The extensive standard library is why Python is described as a language with *"batteries included!"*

:::

--- 
Input, output examples in docstrings are run as tests.

```python
def add(a, b):
    """Add two numbers.
    >>> add(1, 2)
    3
    >>> add(1, 3)
    5
    """
    return a + b
```

 . . .

```bash
$ python -m doctest -v add.py
Failed example:
    add(1, 3)
Expected:
    5
Got:
    4
**********************************************************************
1 items had failures:
   1 of   2 in mod.add
***Test Failed*** 1 failures.
```

::: {.notes}
Doctest can pick up anything that looks like a Python session and run it as a test. 
:::

## Documentation generators

* Sphinx
* mkdocs

::: {.notes}
Sphinx has been around for a long time, has lot's of functionality but is based on reStructuredText. mkdocs is a new kid on the block, based on markdown and has a lot of functionality.
:::

## {background-iframe="https://www.sphinx-doc.org/en/master/"}

## {background-iframe="https://www.mkdocs.org/"}

## mkdocs

* Text is written in markdown
* Easy to use
* API documentation can be generated with mkdocstrings
* The end result is a static website that can be hosted on e.g. GitHub pages

## Configuration

```{.yaml filename="mkdocs.yml"}
site_name: my_library

theme: "material" # or readthedocs, mkdocs, etc.

plugins:
- mkdocstrings:
    handlers:
      python:
        options:
          show_source: false # change if you want able to show source code
          heading_level: 2
          docstring_style: "numpy" # important!, since default is google
```

## API docs {.smaller}

::: {.incremental}

1. install mkdocstrings
    
    `$ pip install mkdocstrings[python]`
2. Install theme, e.g. material
    
    `$ pip install mkdocs-material`
3. Add plugin to mkdocs.yml (see above)
4. Create `index.md` in docs folder
5. Run `mkdocs serve` to view locally
:::

. . . 

`docs/index.md`
```markdown
# Reference

::: my_library.simulation
```

## 

![](images/mkdocs_api.png)


## GitHub pages 

::: {.incremental}

* Once you have a static website, you need to share it with the world
* GitHub pages allows you to easily host a static website on GitHub
* The website is available at `https://dhi.github.io/<repository>/`
* The website can be created locally by manually editing html pages.
* For use as documentation, it is easier to use a documentation generator like mkdocs.

:::

## GitHub pages

![](images/github_pages.png)

## "Private" website

* A GitHub repository can be made private
* The website is still publicly available
* In order to "hide" it from search engines, add a `robots.txt` file to the root of the website
* This is **not** a secure way to hide a website, but it is a simple way to hide it from search engines.

```{.txt filename="robots.txt"}
User-agent: *
Disallow: /
```


## Additional resources

* https://realpython.com/python-project-documentation-with-mkdocs/


## Summary

::: {.incremental}

* Documentation is important
* Use a README file
* Use docstrings 
* Use type hints
* Use `mkdocs` to generate API documentation

:::