This Python package generates charts of CO₂ emissions, fossil fuel production, energy consumption, and electricity trends for the world and specified countires. Example charts are displayed at https://www.worldenergydata.org/.

This package requires installation of Python and dependancies listed below. Charts are output in SVG format so there's no loss of resolution when magnified.

This package consists of -

1. world_energy_data.py (main executable)
2. user_globals.py (global definitions)
3. collate.py (extracts and arranges country specific data from input dataset)
4. process.py (calculations)
5. output.py (chart calls)
6. chart.py (chart functions)
7. countries.py (country name translations to make them compatible with IEA dataset)

Dependencies -

1. Install the latest Python from https://www.python.org/downloads/, scroll down to "Information about specific ports, and developer info" and select suitable platform.
2. Install the following Python libraries by entering the following commands in a terminal:
   a) pip3 install openpyxl
   b) pip3 install git+https://github.com/chenyulue/matplotlib-extra/
   c) pip3 install mpl_extra
   d) pip3 install matplotlib
   e) pip3 install jmespath
   f) pip3 install pandas
   
3. Install font 'SF Pro' from https://developer.apple.com/fonts/. Delete matplotlib's font cache files to force rebuilding of cache and inclusion of 'SF Pro' - in a terminal enter rm ~/.matplotlib/fontlist*
4. The following datasets are required, which are provided in this package and are listed below for reference only:
   a) Global Carbon Budget in .xlsx format. The version in this repository is required as 2024 GCP projected values have been included, obtained from https://essd.copernicus.org/preprints/essd-2024-519/essd-2024-519.pdf. Original GCB was downloaded from https://globalcarbonbudgetdata.org/latest-data.html
   b) NOAA ESRL CO2 data in CSV format from https://gml.noaa.gov/ccgg/trends/gl_data.html
   c) Energy Institute Statistical Review of World Energy data from -
   https://www.energyinst.org/statistical-review/resources-and-data-downloads
   (Direct link is https://www.energyinst.org/__data/assets/file/0003/1055694/Consolidated-Dataset-Narrow-format.csv)
   d) IEA annual energy balances in JSON format obtained from -
   https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser
   

Instructions (code below is within single quotes) -

1. Choose a country to profile from those listed in the Energy Institute's data listed above.
2. Edit the existing profile commands in world_energy_data.py to include 'profile("Country_Name")', where Country_Name is an exact duplicate of the name of the country selected in (1).
3. Browse one of the IEA JSON files listed in (4) above to identify the IEA's equivalent country name.
4. If the IEA version of Country_Name differs, edit countries.iea_country_name() to translate Country_Name to the IEA equivalent.
5. Save all, and in a terminal, enter the command 'python3 world_energy_data.py'
6. Folders will be created named 'charts CO2' for global CO2 charts (generated on each execution), and 'charts Country_Name' for national, or World, energy charts.
7. If need be, edit flags in user_globals.py to suit user preferences.

Written by Shane White, whitesha@protonmail.com, using Python v3.12.2