### Environment Setup Script for Local Development

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DEPLOYMENT.md

A bash script to automate the setup of the local development environment, including creating a virtual environment and installing dependencies.

```bash
#!/bin/bash
# setup-dev-env.sh

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install Python dependencies
pip install pyyaml requests  # requests for team_query.py

# Verify Hugo version
hugo version

# Set GitHub token if using team_query.py
# export GH_TOKEN=ghp_xxxxxxxxxxxx

echo "Development environment ready!"
```

--------------------------------

### Local Development Environment Setup

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DEPLOYMENT.md

Commands to set up a local development environment, including installing required versions of Python and Hugo, and installing Python dependencies.

```bash
# Install required versions
python --version          # Should be 3.13+
hugo version             # Should be 0.152.2+ extended
pip install pyyaml

# Optional: Install Dart Sass (Netlify downloads it)
# On macOS with Homebrew: brew install sass/sass/dart-sass

# Build locally
make html

# Serve locally
make serve
```

--------------------------------

### Shell Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example configuration for the interactive shell section, including intro text and a link to documentation.

```yaml
shell:
  intro:
    - title: Try NumPy
      text: Use the interactive shell to try NumPy in the browser
  docslink: Don't forget to check out the <a href=\"...\">docs</a>.
```

--------------------------------

### Start Development Server with Make

Source: https://github.com/numpy/numpy.org/blob/main/README.md

Starts the development web server using the 'make serve' command. This is the primary way to preview website changes locally during development.

```bash
make serve
```

--------------------------------

### ArrayLibraries Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example configuration for the ArrayLibraries section, including intro text, headers, and library entries.

```yaml
arraylibraries:
  intro:
    - text: NumPy's API is the starting point when libraries are written...
  headers:
    - text: Array Library
    - text: Capabilities & Application areas
  libraries:
    - title: Dask
      text: Distributed arrays and advanced parallelism...
      img: /images/content_images/arlib/dask.png
      alttext: Dask
      url: https://dask.org/
```

--------------------------------

### Example Hero Section Configuration

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

Provides an example of configuring the homepage hero section with specific text, links, and an image.

```yaml
hero:
  title: NumPy
  subtitle: The fundamental package for scientific computing with Python
  buttontext: "Latest release: NumPy 2.4. View all releases"
  buttonlink: "/news/#releases"
  image: logo.svg
```

--------------------------------

### CaseStudy Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example of a CaseStudy entry for the 'First Image of a Black Hole' case study.

```yaml
- title: First Image of a Black Hole
  text: How NumPy enabled the Event Horizon Telescope to produce...
  img: /images/content_images/case_studies/blackhole.png
  alttext: First image of a black hole...
  url: /case-studies/blackhole-image
```

--------------------------------

### Example Navigation Items

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

Illustrates how to configure top navigation menu items using the NavItem structure.

```yaml
navbar:
  - title: Install
    url: /install
  - title: Documentation
    url: https://numpy.org/doc/stable
```

--------------------------------

### Start Local Development Server with Make Serve

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Starts a local development server with hot reloading. Access the site at http://localhost:1313. Stop the server by pressing Ctrl+C.

```bash
make serve
```

```bash
make serve BASEURL=http://localhost:1313
```

--------------------------------

### Install NumPy with uv

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Use uv, a modern Python package manager, for a fast and simple installation.

```bash
uv pip install numpy
```

--------------------------------

### CaseStudies Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example configuration for the CaseStudies section, featuring a title and a list of case studies.

```yaml
casestudies:
  title: CASE STUDIES
  features:
    - title: First Image of a Black Hole
      text: How NumPy, together with libraries like SciPy...
      img: /images/content_images/case_studies/blackhole.png
      alttext: First image of a black hole...
      url: /case-studies/blackhole-image
```

--------------------------------

### Example Navbar Logo Configuration

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

Provides an example of configuring the site's primary logo and text displayed in the top-left corner of the navbar.

```yaml
navbarlogo:
  image: logo.svg
  text: NumPy
  link: /
```

--------------------------------

### Install NumPy with Pip

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Install NumPy using pip, the standard Python package installer, directly into the active environment.

```bash
pip install numpy
```

--------------------------------

### Install NumPy with Conda

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Install NumPy using conda, a popular package and environment manager. This example creates and activates a new environment before installing.

```bash
conda create -n my-env
conda activate my-env
conda install numpy
```

--------------------------------

### Clone Repository and Start Development Server

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/QUICK_REFERENCE.md

Clone the numpy.org repository, prepare submodules, and start the local development server. Visit http://localhost:1313 to see the site.

```bash
git clone https://github.com/numpy/numpy.org.git
cd numpy.org
make prepare
make serve
```

--------------------------------

### LibraryEntry Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example of a LibraryEntry for the Dask library.

```yaml
- title: Dask
  text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale.
  img: /images/content_images/arlib/dask.png
  alttext: Dask
  url: https://dask.org/
```

--------------------------------

### Numpy.org Development Workflow Quick Start

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/INDEX.md

This snippet outlines the essential steps for setting up, developing, testing, building, and deploying changes to the Numpy.org website.

```bash
# 1. Setup
git clone https://github.com/numpy/numpy.org.git
cd numpy.org
make serve

# 2. Make changes
# Edit content in content/en/, layouts/, etc.

# 3. Test locally
# Visit http://localhost:1313

# 4. Build for production
make html

# 5. Deploy
git add .
git commit -m "Description"
git push origin feature-branch
# Create PR, Netlify builds preview
# Merge to main for production deploy

```

--------------------------------

### Local Development Workflow

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Clone the repository, navigate to the directory, and run 'make serve' to start a local development server. The browser will automatically refresh on content edits.

```bash
# Initial setup
git clone https://github.com/numpy/numpy.org.git
cd numpy.org
make serve

# Site is now running at http://localhost:1313
# Edit content files and watch browser refresh
```

--------------------------------

### Install NumPy using a Virtual Environment with Pip

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Set up a virtual environment and install NumPy within it using pip for better dependency management.

```bash
python -m venv my-env
source my-env/bin/activate  # macOS/Linux
my-env\Scripts\activate     # Windows
pip install numpy
```

--------------------------------

### Install NumPy with Homebrew (macOS)

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Install NumPy using the Homebrew package manager on macOS.

```bash
brew install numpy
```

--------------------------------

### Verify NumPy Installation

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Verify that NumPy has been installed correctly by importing it and printing its version.

```python
import numpy as np
print(np.__version__)
```

--------------------------------

### Machine Learning Section Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example demonstrating the usage of the MachineLearning type, with sample content for its 'paras' field.

```yaml
machinelearning:
  paras:
    - para1: NumPy forms the basis of powerful machine learning libraries...
      para2: Statistical techniques called ensemble methods...
```

--------------------------------

### Example Case Studies Data

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/partials.md

An example of the 'casestudies' data structure, showing how to define a case study with a title, description, image, and URL.

```yaml
casestudies:
  title: CASE STUDIES
  features:
    - title: "First Image of a Black Hole"
      text: "How NumPy enabled the Event Horizon Telescope..."
      img: "/images/content_images/case_studies/blackhole.png"
      alttext: "First image of a black hole..."
      url: "/case-studies/blackhole-image"
```

--------------------------------

### Visualization Section Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example of the Visualization type, featuring a preview image with its URL and alt text, and introductory content text.

```yaml
visualization:
  images:
    - url: https://www.fusioncharts.com/blog/...
      img: /images/content_images/v_matplotlib.png
      alttext: A streamplot made in matplotlib
  content:
    - text: NumPy is an essential component in the burgeoning...
```

--------------------------------

### Install NumPy with Chocolatey (Windows)

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Install NumPy using the Chocolatey package manager on Windows.

```bash
choco install numpy
```

--------------------------------

### Example Footer Configuration

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

Illustrates a complete footer configuration, including logo, social media links, and organized quick links across three columns.

```yaml
footer:
  logo: logo.svg
  socialmediatitle: ""
  socialmedia:
    - link: https://github.com/numpy/numpy
      icon: github
  quicklinks:
    column1:
      title: ""
      links:
        - text: Install
          link: /install
        - text: Documentation
          link: https://numpy.org/doc/stable
    column2:
      links:
        - text: About us
          link: /about
    column3:
      links:
        - text: Get help
          link: /gethelp
```

--------------------------------

### Troubleshoot Site Build Issues

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/QUICK_REFERENCE.md

Verify Hugo and Python installations, ensure `pyyaml` is installed, and try regenerating the config and performing a clean build if the site fails to build.

```bash
hugo version
python --version
pip install pyyaml
python gen_config.py
make clean
make html
```

--------------------------------

### Example Social Media Links

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

Shows how to configure social media links for the footer, specifying the URL and icon for each platform.

```yaml
socialmedia:
  - link: https://github.com/numpy/numpy
    icon: github
  - link: https://www.youtube.com/@NumPy_team
    icon: youtube
```

--------------------------------

### Example Array Libraries Data

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/partials.md

An example of the 'arraylibraries' data structure as it might appear in a content file (e.g., content/en/tabcontents.yaml).

```yaml
arraylibraries:
  intro:
    - text: "NumPy's API is the starting point when libraries are written..."
  headers:
    - text: "Array Library"
    - text: "Capabilities & Application areas"
  libraries:
    - title: "Dask"
      text: "Distributed arrays and advanced parallelism for analytics..."
      img: "/images/content_images/arlib/dask.png"
      alttext: "Dask"
      url: "https://dask.org/"
```

--------------------------------

### Install NumPy with APT (Linux)

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Install NumPy using the APT package manager on Debian-based Linux distributions.

```bash
sudo apt install python3-numpy
```

--------------------------------

### Start Development Server without Make

Source: https://github.com/numpy/numpy.org/blob/main/README.md

Alternative method to start the development server for environments without 'make'. It involves running a Python script to generate configuration followed by the Hugo server command.

```bash
python gen_config.py
hugo server
```

--------------------------------

### Scientific Domains Section Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example of the ScientificDomains type, showing introductory text and a list containing a 'Quantum Computing' domain entry.

```yaml
scientificdomains:
  intro:
    - text: Nearly every scientist working in Python draws on NumPy...
  libraries:
    - title: Quantum Computing
      alttext: A computer chip.
      img: /images/content_images/sc_dom_img/quantum_computing.svg
      links:
        - url: https://qutip.org
          label: QuTiP
```

--------------------------------

### Hugo Base Parameters Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example demonstrating the usage of Hugo base parameters, including color scheme, author details, images, navigation color, font settings, and Plausible analytics domain.

```yaml
params:
  colorScheme: light
  author:
    name: "NumPy team"
  images:
    - /images/numpy-image.jpg
  navColor: blue
  font:
    name: "Lato"
    sizes: [400, 900]
  plausible:
    dataDomain: numpy.org
```

--------------------------------

### Scientific Domain Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example of a ScientificDomain entry, specifying details for 'Quantum Computing' including its icon and related library links.

```yaml
- title: Quantum Computing
  alttext: A computer chip.
  img: /images/content_images/sc_dom_img/quantum_computing.svg
  links:
    - url: https://qutip.org
      label: QuTiP
    - url: https://pyquil-docs.rigetti.com/en/stable
      label: PyQuil
```

--------------------------------

### Content Frontmatter Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example of frontmatter for a Markdown file, specifying the title, meta description, draft status, and publication date.

```yaml
---
title: Installation Guide
description: How to install NumPy on different platforms
draft: false
date: 2024-01-15
---

Page content here...
```

--------------------------------

### Install NumPy with pixi

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Use pixi, a cross-platform package manager, to add NumPy to your project.

```bash
pixi add numpy
```

--------------------------------

### Render Shell Lesson Partial

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/partials.md

This partial is used to display the default code example in the interactive shell. It returns a Python code block formatted as a markdown string.

```html
{{ partial "shell-lesson.html" | print | markdownify}}
```

--------------------------------

### Extended Development Server with Make Serve-Dev

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Starts an extended development server with additional flags for active development, such as `--disableFastRender` and `--poll 1000ms`. Use this when the standard `serve` command misses changes or rebuilds are incomplete.

```bash
make serve-dev
```

--------------------------------

### Update Everything (Dependencies, Teams, Serve)

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

A comprehensive command to update Git submodules, refresh team information using a GitHub token, and start the local development server.

```bash
git submodule update --remote
export GH_TOKEN=ghp_xxxxxxxxxxxx
make teams
make serve
```

--------------------------------

### Clean Build Artifacts with Make Clean

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Removes all build artifacts, primarily the `public/` directory and generated static site. Use this to start a fresh build or reclaim disk space. This operation is destructive.

```bash
make clean
```

--------------------------------

### Default Shell Lesson Code

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/partials.md

The default Python code example provided by the shell-lesson partial. It includes instructions for users and demonstrates basic NumPy operations.

```python
"""
To try the examples in the browser:
1. Type code and press Shift + Enter
2. Or copy paste and click "Run" button
"""

import numpy as np

x = np.arange(15, dtype=np.int64).reshape(3, 5)
x[1:, ::2] = -99
x

rng = np.random.default_rng()
samples = rng.normal(size=2500)
samples
```

--------------------------------

### Netlify Build Command

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DEPLOYMENT.md

The sequence of commands executed by Netlify to build the site, including downloading Dart Sass, setting up the PATH, installing dependencies, and running the Hugo build.

```bash
export DART_SASS_TARBALL="dart-sass-${DART_SASS_VERSION}-linux-x64.tar.gz" && \
curl -LJO ${DART_SASS_URL}/${DART_SASS_VERSION}/${DART_SASS_TARBALL} && \
tar -xf ${DART_SASS_TARBALL} && \
rm ${DART_SASS_TARBALL} && \
export PATH=/opt/build/repo/dart-sass:$PATH && \
pip install pyyaml && \
make html
```

--------------------------------

### Netlify Build Configuration

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/CONFIGURATION.md

This TOML file defines the build environment for Netlify, specifying Python, Hugo, and Dart Sass versions, and a custom build command to install dependencies, compile Sass, and build the Hugo site.

```toml
[build.environment]
  PYTHON_VERSION = "3.13"
  HUGO_VERSION = "0.152.2"
  DART_SASS_VERSION = "1.93.2"
  DART_SASS_URL = "https://github.com/sass/dart-sass/releases/download/"

[build]
  base = "/"
  publish = "public"
  command = """
    export DART_SASS_TARBALL="dart-sass-${DART_SASS_VERSION}-linux-x64.tar.gz" && \
    curl -LJO ${DART_SASS_URL}/${DART_SASS_VERSION}/${DART_SASS_TARBALL} && \
    tar -xf ${DART_SASS_TARBALL} && \
    rm ${DART_SASS_TARBALL} && \
    export PATH=/opt/build/repo/dart-sass:$PATH && \
    pip install pyyaml && \
    make html
  """

[context.deploy-preview.environment]
  NUMPYORG_WITH_TRANSLATIONS = "1"

[[plugins]]
  package = "netlify-plugin-checklinks"
  [plugins.inputs]
    todoPatterns = [" public/pt/user-surveys", " public/ja/user-surveys"]
    skipPatterns = ["https://fonts.gstatic.com", "https://fonts.googleapis.com"]
```

--------------------------------

### Build Site for All Languages

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/QUICK_REFERENCE.md

Run the development server with the `NUMPYORG_WITH_TRANSLATIONS=1` flag to include all languages in the preview. Alternatively, build a specific language site.

```bash
make serve-dev NUMPYORG_WITH_TRANSLATIONS=1
```

```bash
make html BASEURL=http://localhost:1313/pt/
```

--------------------------------

### Initialize Project with Make Prepare

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Initializes the project by updating git submodules and generating Hugo configuration. Use this before building or serving for the first time, or after pulling changes that update submodules.

```bash
make prepare
```

--------------------------------

### Troubleshooting ImportError

Source: https://github.com/numpy/numpy.org/blob/main/content/en/install.md

Example of a common ImportError message encountered when NumPy's C extensions fail to import, often due to setup issues.

```text
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!

Importing the numpy c-extensions failed. This error can happen for
different reasons, often due to issues with your setup.
```

--------------------------------

### Data Science Section Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example illustrating the DataScience type, showcasing an introduction, image references, and text blocks for examples and content.

```yaml
datascience:
  intro: NumPy lies at the core of a rich ecosystem...
  image1:
    - img: /images/content_images/ds-landscape.png
      alttext: Diagram of Python Libraries...
  image2:
    - img: /images/content_images/data-science.png
      alttext: Diagram of three overlapping circles...
  examples:
    - text: "<b>Extract, Transform, Load:</b> [Pandas](...)..."
  content:
    - text: "For high data volumes, Dask and Ray..."
```

--------------------------------

### Tabs Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

An example of how to use the Tabs type to define a section title.

```yaml
tabs:
  title: ECOSYSTEM
```

--------------------------------

### Build for Production

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Generate the production-ready static site. Ensure the correct BASEURL is set for the production environment.

```bash
make html BASEURL=https://numpy.org
```

--------------------------------

### Install pyyaml Dependency

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Use this command to install the pyyaml package if you encounter a 'ModuleNotFoundError'.

```bash
pip install pyyaml
```

--------------------------------

### Build Static Site for Production with Make HTML

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Builds the static site to the `public/` directory for production deployment. This is equivalent to running `hugo`. Use this before deployment or to preview the final output locally.

```bash
make html
```

```bash
make html BASEURL=https://staging.numpy.org
```

--------------------------------

### Debug Configuration

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Prepare the build environment and display the first 50 lines of the generated configuration file. Useful for debugging build configuration issues.

```bash
make prepare
cat config.yaml | head -50
```

--------------------------------

### Build and Serve Commands

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/CONFIGURATION.md

Commands for generating configuration from a template, building the Hugo site, and serving it locally with live changes detection.

```bash
# Generate config from template (required before build)
python gen_config.py

# Build site
hugo

# Or serve locally with changes detected
hugo server -D

# Build with custom base URL (for staging)
make html BASEURL=https://staging.numpy.org
```

--------------------------------

### NumPy.org Build Command Reference

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/QUICK_REFERENCE.md

Lists common Make commands for serving the development server, building for production, and utility operations.

```bash
# Development
make serve              # Start dev server
make serve-dev          # Dev server with full rebuild

# Production
make html               # Build for production
make html BASEURL=URL   # Build with custom base URL

# Utilities
make prepare            # Setup (init submodules, generate config)
make clean              # Remove build artifacts
make teams              # Generate team pages (needs GH_TOKEN)
make help               # Show all targets
```

--------------------------------

### Full Rebuild from Scratch

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Perform a complete rebuild of the site by cleaning previous build artifacts and then generating the HTML output.

```bash
make clean
make html
```

--------------------------------

### Netlify Deploy Preview Environment Variables

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DEPLOYMENT.md

Configuration for the deploy-preview context to enable all language translations for testing purposes.

```toml
[context.deploy-preview.environment]
  NUMPYORG_WITH_TRANSLATIONS = "1"
```

--------------------------------

### Generate Team Gallery

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/QUICK_REFERENCE.md

Set your GitHub token as an environment variable and run `make teams` to generate the team gallery. Verify the output in the `content/en/teams/` directory.

```bash
export GH_TOKEN=ghp_xxxxxxxxxxxx
make teams
```

--------------------------------

### Production Deployment Workflow

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Perform a local verification build using 'make clean' and 'make html', then push changes to GitHub. Netlify automatically handles the production build and deployment.

```bash
# Local verification
make clean
make html

# Verify output in public/ directory
ls -la public/

# Push to GitHub
git add .
git commit -m "Update content"
git push origin main

# Netlify automatically builds using netlify.toml settings
```

--------------------------------

### Embedded NumPy Code Example

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/partials.md

An example of Python code pre-loaded into the interactive shell, demonstrating NumPy array creation and manipulation. This code is executed within the JupyterLite environment.

```python
import numpy as np
x = np.arange(15, dtype=np.int64).reshape(3, 5)
x[1:, ::2] = -99
samples = np.random.default_rng().normal(size=2500)
```

--------------------------------

### Netlify Branch Deploy Context Configuration

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DEPLOYMENT.md

Optional configuration to enable building for specific branches using a custom command.

```toml
[context.branch-deploy]
  command = "make html"
```

--------------------------------

### Perform Full Site Build Test

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/QUICK_REFERENCE.md

Clean the build output, perform a full static site build, and then list the contents of the public directory to verify the build.

```bash
make clean
make html
ls -la public/
```

--------------------------------

### Display Available Make Targets

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/build-system.md

Lists all available Make targets and their descriptions. This is useful for understanding the project's build capabilities.

```bash
make help
```

--------------------------------

### Embedding Shortcode in Markdown

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/shortcodes.md

An example of embedding a 'sponsors' shortcode within a Markdown file.

```markdown
# Section Title

Some introductory text.

{{< sponsors >}}

More content here...
```

--------------------------------

### Root Configuration Files Overview

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DIRECTORY_STRUCTURE.md

Lists and describes the primary configuration files located at the root of the project, including Hugo, Netlify, Git, and build automation settings.

```markdown
| File | Purpose | Format |
|------|---------|--------|
| `config.yaml.in` | Base Hugo configuration template | YAML |
| `config.yaml` | Generated Hugo configuration (git-ignored) | YAML |
| `netlify.toml` | Netlify build and deployment settings | TOML |
| `Makefile` | Build automation targets | Makefile |
| `.gitignore` | Git ignore patterns | Text |
| `.gitmodules` | Git submodule configuration | Text |

```

--------------------------------

### Data Science Type Definition

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/TYPES.md

Defines the structure for data science content, including an introduction, image blocks, examples, and content paragraphs.

```yaml
DataScience:
  intro: string
  image1: ImageBlock[]
  image2: ImageBlock[]
  examples: TextBlock[]
  content: TextBlock[]
```

--------------------------------

### Asset Path References in Content

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DIRECTORY_STRUCTURE.md

Examples of how to reference various assets, such as images, CSS, and JavaScript, within the content and templates of the Numpy.org website.

```markdown
# Images in static/
![Alt text](/images/content_images/numpy-comic.png)

# Logos
![Logo](/images/logos/berkeley-color.svg)

# Case study images
![Image](/images/content_images/case_studies/blackhole.png)

# CSS in assets/css/
<link rel="stylesheet" href="/css/style.css">

# JavaScript in assets/js/
<script src="/js/app.js"></script>
```

--------------------------------

### Development Workflow for NumPy.org

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/DEPLOYMENT.md

Follow these steps to make local changes, test them, and prepare them for a pull request. Ensure you are on a feature branch before making changes.

```bash
git checkout -b feature/my-changes
# Edit content, layouts, config, etc.
make serve
# Visit http://localhost:1313
make clean
make html
git add .
git commit -m "Add feature"
git push origin feature/my-changes
```

--------------------------------

### Hugo Extended Version Error

Source: https://github.com/numpy/numpy.org/blob/main/README.md

This error message indicates that the Hugo extended version is not installed. Ensure you have the extended version of Hugo to avoid this issue.

```bash
error: failed to transform resource: TOCSS: failed to transform "style.sass"
```

--------------------------------

### Call sendThankYou After Form Submission

Source: https://github.com/numpy/numpy.org/blob/main/_autodocs/api-reference/frontend.md

Example of how to invoke the `sendThankYou` function within a form submission's success callback, typically after an API call.

```javascript
// When form submission completes successfully
fetch('/api/subscribe', { method: 'POST', body: formData })
  .then(response => response.json())
  .then(data => {
    if (data.success) {
      sendThankYou();
    }
  });
```