Reconstruct paleolocations of present-day locations and shapes across the Phanerozoic. PyGplates allows to use some of the functionality of the GUI GPlates software within python scripts. This allows scripting to automatically process many different locations and/or time periods. Different rotation models can be used and are easily exchangeable. I currently use the PALEOMAP rotation model that is consistent to the Bristol Scotese simulations by Paul Valdes.
Present-day locations for pygplates_paleolocations.ipynb need to be defined in a CSV file (name, lat, lon) as shown in the example file inpt_sites_example.csv. When reconstructing shapes (e.g. a country outline) with pygplates_paleoshapes.ipynb each shape needs to be defined in a separate CSV file (name, lat, lon) with the points describing the modern outline of the shape. We also need to specify the location of the chosen plate model files, e.g. 'data/PALEOMAP_Global_Plate_Model' (download link). The PyGPlates software itself is not yet availabe via packet managers like conda. Pre-compiled binaries are available for different operating systems and python versions. This repo was used on macOS and google colab using the conda environemt specified in environemnt.yml (see below) with the respective bianaries in the bin directory. These need to be updated accordingly when running in a different environemnt.
Reconstructed paleolocations are saved both in CSV format and as shape files (for e.g. easy batch plotting in python). Seperate output directories are created for each specified reconstruction time.
Notebooks can either be run on Google Colab (online, Google account required) or locally. Notebooks should include a button to open it in Colab, otherwise you can also directly load a GitHub repo within Colab. Easiest way to run locally is to first download the repo with
git clone https://github.com/USERNAME/REPOSITORY
and then install conda (if not installed already). Then create an environment env_name with
conda env create --name env_name --file=environment.yml
using the environment.yml file from this repository to install all necessary python packages. The notebooks can then be run interactively by typing
jupyter lab
We further use the jupytext package to automatically save a pure python script (*.py) each time the notebook is saved. This is very helpful for clean diffs in the version control and allows you to run the analysis in your local terminal with:
python notebook_name.py
The python file can also be shared with others to work on the code together using all the version control benefits (branches, pull requests, ...). You can edit it with any tex editor/IDE and it can also be converted back to a jupyter notebook (with no output) via
jupytext --to notebook notebook_name.py