Analyzing Global CH4 Emissions with Jupyter Book

This repository contains a comprehensive Jupyter Book project analyzing global methane (CH4) emissions from rice paddies. It includes data exploration, replication of academic papers, hypothesis testing, and geospatial analysis to better understand methane emission estimates and their discrepancies.

Features

• Data exploration and visualization of methane emissions data.
• Replication and testing of the University of Malaysia's methane emissions paper.
• Hypothesis testing on emission data distributions.
• Geospatial merging and mapping of methane emission data.
• Automated build pipeline for the Jupyter Book site.

Tech Stack

• Primary language: Jupyter Notebooks (Python)
• Key libraries: pandas, geopandas, matplotlib, scipy, folium
• Build tools: Jupyter Book, Python subprocess for automation

Getting Started

Prerequisites

• Python 3.x
• pip package manager

Installation

1. Clone the repository:

git clone https://github.com/justin-napolitano/ch4-emissions.git
cd ch4-emissions

2. Install dependencies:

pip install -r requirements.txt

Build and Run

The project uses a Python build script to automate dependency installation and Jupyter Book build:

python python_build.py

Alternatively, you can manually build the Jupyter Book:

jupyter-book build jupyter-book

Open the generated HTML files in the jupyter-book/_build/html directory to view the reports.

Project Structure

ch4-emissions/
├── data/                 # Raw and processed data files
├── jupyter-book/         # Jupyter Book source files
│   ├── notebooks/        # Analysis notebooks
│   ├── _config.yml       # Jupyter Book configuration
│   ├── _toc.yml          # Table of contents
│   └── index.md          # Book introduction
├── python_build.py       # Build and deployment automation script
├── website/              # Website related files (assumed)

Future Work / Roadmap

• Add detailed documentation and descriptions for each notebook.
• Expand the geospatial analysis with more datasets.
• Improve automation scripts for deployment.
• Integrate more robust testing and validation of emission models.