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Finalize Jupyter Book + TOC + notebook IDs
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‎.binder/environment.yml‎

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name: comp-analysis-binder
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channels:
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- conda-forge
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- defaults
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dependencies:
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- python>=3.10
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- pip
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- pip:
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- -r requirements.txt

‎.devcontainer/devcontainer.json‎

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{
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"name": "Computational Analysis Course",
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"image": "mcr.microsoft.com/devcontainers/python:3.11",
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"features": {
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"ghcr.io/devcontainers/features/common-utils:2": {}
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},
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"postCreateCommand": "pip install --upgrade pip && pip install -r requirements.txt",
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"customizations": {
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"vscode": {
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"settings": {
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"python.defaultInterpreterPath": "/usr/local/bin/python",
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"jupyter.askForKernelRestart": false
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},
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"extensions": [
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"ms-python.python",
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"ms-toolsai.jupyter",
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"github.vscode-pull-request-github"
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]
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}
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},
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"remoteUser": "vscode"
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}

‎.github/workflows/deploy-book.yml‎

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name: Deploy Jupyter Book
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on:
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push:
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branches: [ "main" ]
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workflow_dispatch:
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jobs:
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build-deploy:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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- name: Install deps
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Build Jupyter Book
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run: |
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jupyter-book build book
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- name: Deploy to GitHub Pages
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uses: peaceiris/actions-gh-pages@v3
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}
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publish_dir: ./book/_build/html
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publish_branch: gh-pages

‎.vscode/settings.json‎

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{
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"python.analysis.typeCheckingMode": "basic",
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"jupyter.jupyterServerType": "local",
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"editor.rulers": [
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88
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]
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}

‎LICENSE‎

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MIT License
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Copyright (c) 2025
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎Makefile‎

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.PHONY: book clean
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BOOK?=book
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JB:=$(shell python -c 'import sysconfig, os; print(os.path.join(sysconfig.get_path("scripts"), "jupyter-book"))')
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book:
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"$(JB)" build $(BOOK)
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clean:
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rm -rf $(BOOK)/_build

‎README.md‎

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# computational-analysis-course
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# Computational Analysis (Undergraduate Level)
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A complete, self-contained course for undergraduate **Computational Analysis** using Python.
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Designed to run seamlessly in **GitHub Codespaces**, locally, or via Binder.
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## 🔰 Audience & Prereqs
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- Undergraduates in engineering / data science / applied math.
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- Comfortable with calculus, linear algebra, and basic Python (variables, loops, functions).
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## 🎯 Learning Outcomes
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By the end of this course, you will be able to:
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- Use Python (NumPy, Pandas) for numerical computation and data analysis.
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- Implement and analyze simple numerical methods (root-finding, linear algebra, ODE/PDE basics).
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- Build and evaluate basic ML models (linear regression) for empirical modeling.
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- Create clear visualizations with Matplotlib.
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- Practice reproducible, documented, version-controlled computational workflows (Jupyter Book).
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## 🗂 Repository Layout
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```
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.
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├─ book/ # Jupyter Book source
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│ ├─ _config.yml
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│ ├─ _toc.yml
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│ └─ content/ # Markdown pages & notebook references
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├─ notebooks/ # Jupyter notebooks (hands-on labs)
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│ ├─ 01_python_basics.ipynb
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│ ├─ 02_numpy_linear_algebra.ipynb
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│ ├─ 03_pandas_data_analysis.ipynb
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│ ├─ 04_matplotlib_visualization.ipynb
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│ ├─ 05_optimization_gradient_descent.ipynb
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│ ├─ 06_cfd_1d_heat_equation.ipynb
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│ └─ 07_ml_linear_regression_sklearn.ipynb
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├─ data/
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│ └─ air_quality_sample.csv
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├─ courseutils/ # Small helper package used by notebooks
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│ ├─ __init__.py
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│ └─ plotting.py
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├─ .devcontainer/
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│ └─ devcontainer.json # Codespaces config
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├─ .github/workflows/
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│ └─ deploy-book.yml # Build & publish Jupyter Book to GitHub Pages
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├─ .vscode/settings.json
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├─ .binder/environment.yml # Binder/Hub environment (optional)
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├─ environment.yml # Conda-style env (optional)
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├─ requirements.txt # pip deps
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├─ syllabus.md # Detailed syllabus & weekly plan
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├─ LICENSE
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└─ Makefile # Quality-of-life commands
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```
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## 🚀 Quickstart (Codespaces)
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1. Click **Code → Create codespace on main**.
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2. After the container builds, the post-create step installs dependencies.
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3. Open any notebook in `notebooks/` and run.
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4. To build the Jupyter Book site locally:
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```bash
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make book
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```
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## 📦 Local Install
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```bash
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python -m venv .venv
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source .venv/bin/activate # Windows: .venv\Scripts\activate
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pip install -r requirements.txt
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jupyter lab
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```
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## 🌐 Build & Publish the Course Website
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- Push to GitHub; the **deploy-book** workflow builds & deploys the book to **gh-pages**.
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- Enable GitHub Pages in repo settings: Source = `Deploy from a branch` → Branch = `gh-pages`.
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## 🧑‍🏫 Instructor Notes
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- The course is modular; assign notebooks as labs or homework.
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- Use `syllabus.md` for a week-by-week plan with outcomes and assessments.
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- Keep datasets small in-repo; link to larger data if needed.
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## 🔗 License
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MIT — see `LICENSE`.

‎_build/logs/myst.build.json‎

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{
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"input": {
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"files": [
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"book"
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],
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"opts": {
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"execute": false,
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"pdf": false,
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"tex": false,
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"typst": false,
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"docx": false,
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"md": false,
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"xml": false,
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"meca": false,
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"cff": false,
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"site": false,
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"html": false,
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"all": false,
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"doiBib": false,
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"watch": false,
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"force": false,
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"checkLinks": false,
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"strict": false,
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"ci": false,
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"maxSizeWebp": 1572864,
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"keepHost": false
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}
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},
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"exports": []
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}

‎book/_build/.doctrees/content/getting_started.doctree‎

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‎book/_build/.doctrees/content/index.doctree‎

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