scientific-documentation

name	scientific-documentation
description	Set up and maintain documentation for scientific Python packages. Covers Sphinx, MkDocs, NumPy-style docstrings, Diataxis framework, accessibility standards, and documentation hosting with Read the Docs.
metadata	{"assets":["assets/sphinx-conf-scientific.py","assets/mkdocs-scientific.yml","assets/noxfile-docs.py","assets/readthedocs.yaml","assets/index-template.md"],"references":["references/accessible-documentation.md","references/common-issues.md","references/diataxis-framework.md","references/docstring-examples.md","references/notebook-integration.md","references/sphinx-extensions.md"],"scripts":["scripts/generate-api-docs.py"]}

Scientific Python Documentation

A comprehensive guide to creating documentation for scientific Python packages following community best practices from the Scientific Python Development Guide.

Resources in This Skill

This skill includes extensive supporting materials for documentation tasks:

References (detailed guides):

• references/diataxis-framework.md - Complete Diátaxis framework guide with examples for all four documentation types
• references/sphinx-extensions.md - Detailed Sphinx extension configuration and usage
• references/docstring-examples.md - Comprehensive NumPy-style docstring examples for functions, classes, and modules
• references/notebook-integration.md - Jupyter notebook integration guide for Sphinx and MkDocs
• references/common-issues.md - Troubleshooting documentation build issues
• references/accessible-documentation.md - Accessibility guidelines for scientific documentation including images, color contrast, and alt text

Assets (ready-to-use templates):

• assets/sphinx-conf-scientific.py - Complete Sphinx conf.py template for scientific Python
• assets/readthedocs.yaml - Read the Docs configuration template
• assets/mkdocs-scientific.yml - MkDocs configuration template with Material theme
• assets/noxfile-docs.py - Nox automation sessions for documentation builds
• assets/index-template.md - Documentation landing page template

Scripts:

• scripts/generate-api-docs.py - Script to generate API documentation stubs

Quick Decision Trees

Documentation Framework Selection

Choose your documentation framework based on project needs:

Sphinx - Best for:

• LaTeX/math-heavy documentation
• Established scientific Python ecosystem integration
• Projects similar to NumPy, SciPy, Astropy
• Need for PDF output
• Extensive API documentation with autodoc

MkDocs - Best for:

• Markdown-native documentation
• Simpler setup with fewer plugins
• Projects similar to FastAPI, Typer
• Modern, clean aesthetics
• Rapid prototyping

Jupyter Book - Best for:

• Notebook-centric documentation
• Computational narratives and tutorials
• Interactive examples and visualizations
• Educational content
• Data science workflows

Theme Selection

For Sphinx:

Theme	Best For	Pros	Cons
pydata-sphinx-theme	Scientific Python standard	Dark mode, excellent navigation, ecosystem alignment	Requires configuration
furo	Modern projects	Clean design, excellent search, fast	Less customization
sphinx_rtd_theme	Traditional docs	Widely recognized, simple setup	Older design
sphinx-book-theme	Book-like docs	Beautiful, narrative-focused	Less suitable for API-heavy

For MkDocs:

Theme	Best For	Pros	Cons
material	Modern projects	Feature-rich, beautiful, customizable	Can be overwhelming
readthedocs	Simple needs	Familiar, straightforward	Basic features

Hosting Selection

Platform	Best For	Pros	Cons
Read the Docs	Open source projects	Free, automatic builds, versioning	Limited customization
GitHub Pages	Simple static sites	Simple, integrated with GitHub	Manual build process
Netlify	Advanced needs	Preview deployments, control	Requires setup

Diátaxis Framework

The Diátaxis framework organizes documentation into four categories based on user needs:

Type	Purpose	Characteristics
Tutorials	Help newcomers learn by doing	Progressive difficulty, complete examples, encouraging tone
How-to Guides	Help users accomplish specific goals	Problem-focused, practical recipes, real-world scenarios
Reference	Provide technical specifications	Comprehensive, consistent, auto-generated from docstrings
Explanation	Help users understand concepts	Discursive, contextual, theory and design decisions

Key principle: Don't mix documentation types. Keep tutorials separate from reference docs.

For complete Diátaxis guidance with detailed examples and templates for each documentation type, see references/diataxis-framework.md.

Sphinx Configuration for Scientific Python

Essential Extensions

Create docs/conf.py with these core extensions:

extensions = [
    # Core Sphinx extensions
    "sphinx.ext.autodoc",        # Auto-generate docs from docstrings
    "sphinx.ext.autosummary",    # Generate summary tables
    "sphinx.ext.viewcode",       # Add source code links
    "sphinx.ext.intersphinx",    # Link to other projects

    # Scientific Python extensions
    "sphinx.ext.napoleon",       # NumPy/Google docstrings
    "sphinx.ext.mathjax",        # Math rendering
    "numpydoc",                  # NumPy documentation style
    "sphinx_autodoc_typehints",  # Type hint integration

    # Markdown support
    "myst_parser",               # MyST Markdown parser

    # Notebook integration
    "nbsphinx",                  # Jupyter notebook rendering
]

Extension Purposes

autodoc - Automatically generate documentation from docstrings:

.. automodule:: mypackage.analysis
   :members:
   :undoc-members:
   :show-inheritance:

autosummary - Create summary tables:

.. autosummary::
   :toctree: generated/

   mypackage.function1
   mypackage.function2

napoleon - Parse NumPy-style docstrings (configure in conf.py):

napoleon_google_docstring = False
napoleon_numpy_docstring = True
napoleon_include_init_with_doc = True
napoleon_use_param = True
napoleon_use_rtype = True

intersphinx - Link to external documentation:

intersphinx_mapping = {
    "python": ("https://docs.python.org/3", None),
    "numpy": ("https://numpy.org/doc/stable/", None),
    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
    "pandas": ("https://pandas.pydata.org/docs/", None),
    "matplotlib": ("https://matplotlib.org/stable/", None),
    "xarray": ("https://docs.xarray.dev/en/stable/", None),
    "sklearn": ("https://scikit-learn.org/stable/", None),
}

myst_parser - Use Markdown in Sphinx:

myst_enable_extensions = [
    "colon_fence",    # ::: fences
    "deflist",        # Definition lists
    "dollarmath",     # $...$ and $$...$$ for math
    "fieldlist",      # Field lists
    "substitution",   # Variable substitution
    "tasklist",       # Task lists
]

Theme Configuration

PyData Sphinx Theme (recommended for scientific Python):

html_theme = "pydata_sphinx_theme"

html_theme_options = {
    "github_url": "https://github.com/org/package",
    "use_edit_page_button": True,
    "show_toc_level": 2,
    "navigation_with_keys": True,
    "icon_links": [
        {
            "name": "PyPI",
            "url": "https://pypi.org/project/your-package",
            "icon": "fas fa-box",
        },
    ],
}

html_context = {
    "github_user": "org",
    "github_repo": "package",
    "github_version": "main",
    "doc_path": "docs",
}

For complete Sphinx extension configuration including advanced options, autodoc directives, and troubleshooting, see references/sphinx-extensions.md. For a complete ready-to-use conf.py, see assets/sphinx-conf-scientific.py.

NumPy-style Docstrings

NumPy-style docstrings are the standard for scientific Python projects. Key sections include:

• Short summary - One-line description
• Extended summary - More detailed explanation
• Parameters - Input arguments with types and descriptions
• Returns - Return values with types
• Raises - Exceptions that may be raised
• See Also - Related functions/classes
• Notes - Additional information, math formulas
• Examples - Runnable code examples

Concise Function Example

def compute_statistic(data, method="mean", axis=0, weights=None):
    """
    Compute a statistical measure along the specified axis.

    Parameters
    ----------
    data : array_like
        Input data array. Can be any shape.
    method : {'mean', 'median', 'std'}, optional
        Statistical method to compute. Default is 'mean'.
    axis : int or None, optional
        Axis along which to compute. Default is 0.
    weights : array_like, optional
        Weights for each value. If None, equal weights.

    Returns
    -------
    ndarray
        Computed statistic.

    Raises
    ------
    ValueError
        If `method` is not supported.

    Examples
    --------
    >>> import numpy as np
    >>> data = np.array([1, 2, 3, 4, 5])
    >>> compute_statistic(data, method='mean')
    3.0
    """
    pass

For comprehensive NumPy-style docstring examples including classes, modules, generators, and all supported sections, see references/docstring-examples.md.

Read the Docs Integration

Configuration File: .readthedocs.yaml

Place in repository root:

version: 2

build:
  os: ubuntu-24.04
  tools:
    python: "3.12"
  jobs:
    post_install:
      # Install package with docs dependencies
      - pip install .[docs]

sphinx:
  configuration: docs/conf.py
  fail_on_warning: true

formats:
  - pdf
  - epub

Dependencies

In pyproject.toml, add docs extra:

[project.optional-dependencies]
docs = [
    "sphinx>=7.0",
    "pydata-sphinx-theme>=0.15",
    "sphinx-autodoc-typehints>=2.0",
    "numpydoc>=1.6",
    "myst-parser>=2.0",
    "nbsphinx>=0.9",
]

Or in setup.py:

extras_require = {
    "docs": [
        "sphinx>=7.0",
        "pydata-sphinx-theme>=0.15",
        "sphinx-autodoc-typehints>=2.0",
        "numpydoc>=1.6",
        "myst-parser>=2.0",
        "nbsphinx>=0.9",
    ],
}

Setting up RTD

1. Go to https://readthedocs.org/
2. Import your repository
3. Trigger a build
4. Configure advanced settings:
   • Default version
   • Privacy level
   • Build notifications

MkDocs Configuration for Scientific Python

mkdocs.yml

site_name: My Scientific Package
site_description: Description of your scientific package
site_url: https://org.github.io/package
repo_url: https://github.com/org/package
repo_name: org/package

theme:
  name: material
  features:
    - navigation.tabs
    - navigation.sections
    - navigation.expand
    - navigation.top
    - search.suggest
    - search.highlight
    - content.code.copy
    - content.tabs.link
  palette:
    - scheme: default
      primary: indigo
      accent: indigo
      toggle:
        icon: material/brightness-7
        name: Switch to dark mode
    - scheme: slate
       
       
      
         
            


   
   
      
        
          
             
             
             
             
             
   
       
       


   
       
   
       
       
       
   
   
   
   
       
   
   
   
   
   
   


   
   


    
   
        
        
   
       
        
   
       
        
   
        
        
   
       
        

MathJax Configuration

Create docs/javascripts/mathjax.js:

window.MathJax = {
  tex: {
    inlineMath: [["\\(", "\\)"]],
    displayMath: [["\\[", "\\]"]],
    processEscapes: true,
    processEnvironments: true
  },
  options: {
    ignoreHtmlClass: ".*|",
    processHtmlClass: "arithmatex"
  }
};

document$.subscribe(() => {
  MathJax.typesetPromise()
})

Notebook Integration

With Sphinx (nbsphinx)

Configuration in conf.py:

extensions = [
    # ... other extensions
    "nbsphinx",
]

# nbsphinx configuration
nbsphinx_execute = "auto"  # or "always", "never"
nbsphinx_allow_errors = False
nbsphinx_timeout = 60  # seconds

# Exclude patterns
exclude_patterns = [
    "_build",
    "**.ipynb_checkpoints",
]

Include notebooks in documentation:

.. toctree::
   :maxdepth: 2

   notebooks/tutorial
   notebooks/advanced_usage

With MkDocs (mkdocs-jupyter)

Configuration in mkdocs.yml:

plugins:
  - mkdocs-jupyter:
      include_source: true
      execute: false
      allow_errors: false
      kernel_name: python3

Reference in navigation:

nav:
  - Tutorials:
      - Getting Started: notebooks/tutorial.ipynb
      - Advanced: notebooks/advanced.ipynb

For advanced notebook integration including execution options, cell tags, and troubleshooting, see references/notebook-integration.md.

Build Automation with Nox

noxfile.py

"""Nox sessions for documentation."""
import nox

@nox.session(python="3.12")
def docs(session):
    """Build the documentation."""
    session.install(".[docs]")
    session.run(
        "sphinx-build",
        "-W",  # Treat warnings as errors
        "-b", "html",
        "docs",
        "docs/_build/html",
    )

@nox.session(python="3.12")
def docs_live(session):
    """Build and serve documentation with live reload."""
    session.install(".[docs]", "sphinx-autobuild")
    session.run(
        "sphinx-autobuild",
        "-W",
        "--open-browser",
        "docs",
        "docs/_build/html",
    )

@nox.session(python="3.12")
def docs_linkcheck(session):
    """Check documentation links."""
    session.install(".[docs]")
    session.run(
        "sphinx-build",
        "-b", "linkcheck",
        "docs",
        "docs/_build/linkcheck",
    )

Usage:

nox -s docs        # Build docs
nox -s docs_live   # Live preview
nox -s docs_linkcheck  # Check links

For a complete noxfile with additional sessions (spelling, coverage, doctest, PDF builds), see assets/noxfile-docs.py.

Documentation Structure

Recommended Directory Layout

docs/
├── conf.py                    # Sphinx configuration
├── index.md                   # Landing page
├── getting-started/
│   ├── installation.md
│   └── quickstart.md
├── tutorials/
│   ├── index.md
│   ├── first-analysis.md
│   └── advanced-usage.md
├── guides/
│   ├── index.md
│   ├── large-datasets.md
│   ├── performance.md
│   └── extending.md
├── reference/
│   ├── api.rst
│   ├── cli.md
│   └── configuration.md
├── explanation/
│   ├── index.md
│   ├── architecture.md
│   ├── algorithms.md
│   └── design-decisions.md
├── notebooks/
│   └── *.ipynb
└── _static/
    └── custom.css

Best Practices

Writing Tips

1. Start with the user's goal - What are they trying to accomplish?
2. Use active voice - "The function returns..." not "The return value is..."
3. Include runnable examples - Every example should work
4. Cross-reference liberally - Use :func:, :class:, :ref:
5. Update docs with code - Documentation is code

Common Pitfalls

1. Mixing documentation types - Don't put tutorials in reference docs
2. Outdated examples - Test your examples in CI
3. Missing dependencies - Document all requirements clearly
4. Broken links - Use linkcheck builder
5. No search keywords - Use descriptive titles and headers

For detailed troubleshooting of common documentation build issues, see references/common-issues.md.

Accessibility Guidelines

Accessible documentation ensures all users can effectively use your documentation regardless of ability. Follow these core principles based on the Scientific Python Accessible Documentation Guide.

Core Principles

1. Write it out - Text is highly adaptable across screen readers, search engines, and different devices
2. Provide multiple options - Offer both text explanations and images for concepts
3. Use semantic structures - Use HTML/Markdown elements as intended (headings for hierarchy, lists for related items)

Documentation Structure

• Use headings in hierarchical order without skipping levels (H1 to H2 to H3, not H1 to H3)
• Create descriptive page titles that match navigation labels
• Include table of contents with jump links for long pages
• Provide site search functionality

Text Accessibility

• Write complete sentences with proper grammar and punctuation
• Use plain language; avoid unnecessary jargon
• Define field-specific terms and expand acronyms on first use
• Write descriptive link text (not "click here")

Image Accessibility

Design requirements:

• Images must remain comprehensible in grayscale
• Avoid red-green color combinations that overlap
• Maintain minimum 4.5:1 color contrast for text
• Ensure annotations are readable (similar size to body text)
• Prevent animated flashing (no more than 3 times per second)

Context requirements:

• Provide alt text capturing the information conveyed
• Include detailed image descriptions for complex figures
• All text in images must appear as actual text elsewhere
• Link to source data or notebooks when applicable

Video Accessibility

• Introduce videos contextually before they appear
• Require explicit play/pause controls (no autoplay)
• Include closed captions and provide linked transcripts
• Ensure video content is duplicated in text elsewhere

Quick Accessibility Checklist

• Page titles match navigation labels
• Headings in proper order (no skipping levels)
• Color contrast at least 4.5:1
• Images work in grayscale
• All images have meaningful alt text
• Link text describes destination
• Videos have controls (no autoplay)
• Text alternatives for all non-text content

For comprehensive accessibility guidelines including code examples, testing tools, and detailed checklists, see references/accessible-documentation.md.

Version Documentation

For multiple versions:

# conf.py
version = "0.1"  # Short version
release = "0.1.0"  # Full version

# Add version switcher (pydata theme)
html_theme_options = {
    "navbar_end": ["version-switcher", "navbar-icon-links"],
}

Scientific-Specific Sections

Citation Information

Create CITATION.cff:

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: "Doe"
    given-names: "Jane"
    orcid: "https://orcid.org/0000-0000-0000-0000"
title: "My Scientific Package"
version: 0.1.0
doi: 10.5281/zenodo.1234567
date-released: 2024-01-01
url: "https://github.com/org/package"

Document in docs/citation.md:

# Citing This Software

If you use this software in your research, please cite:

## BibTeX
\`\`\`bibtex
@software{doe2024package,
  author = {Doe, Jane},
  title = {My Scientific Package},
  year = {2024},
  version = {0.1.0},
  doi = {10.5281/zenodo.1234567},
  url = {https://github.com/org/package}
}
\`\`\`

## APA
Doe, J. (2024). My Scientific Package (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.1234567

Reproducibility Guide

# Reproducibility

## Environment Setup

Install exact versions:
\`\`\`bash
pip install package==0.1.0
\`\`\`

Or from `requirements.txt`:
\`\`\`bash
pip install -r requirements.txt
\`\`\`

## Random Seeds

For reproducible results:
\`\`\`python
import numpy as np
import random

random.seed(42)
np.random.seed(42)
\`\`\`

## Data Requirements

Sample data available at: [URL]

Expected data format:
- CSV with columns: [...]
- Missing values encoded as NaN
- Date format: ISO 8601

## System Requirements

- Python 3.9+
- 8GB RAM minimum
- 16GB RAM recommended for large datasets

Resources

Primary References

• Scientific Python Documentation Guide
• Diátaxis Framework
• NumPy Documentation Style Guide

Tooling Documentation

• Sphinx
• MyST Parser
• MkDocs Material
• Read the Docs

Example Projects

• NumPy
• SciPy
• Xarray
• scikit-learn