User Guide¶
This user guide covers essential features of gospl, mostly in the form of interactive Jupyter notebooks and Python scripts. Reading this guide, you will learn:
data structure used in gospl input file,
how to generate initial conditions like topography, precipitation and tectonic maps to force a simulation,
how to extract some of the output from the model results to visualise them in Jupyter notebooks,
how to run sequence of backward/forward gospl models using Python functions,
how to set a script for running gospl on HPC.
Notebooks cover just a small selection of functions as an illustration of principles. For a full overview of gospl capabilities, head to the API reference. For additional examples, you might be interested in the following set of examples available from the Stellar-SFM project.
Step 1 - The input file¶
Imposing initial conditions, specifying physical processes parameters and understanding how the input file is structured...
Step 2 - Tutorials via Jupyter notebooks¶
Installing additional libraries & the examples¶
Here we assume that you have followed one of the methods described in the Getting Started guide and have successfully installed gospl either via pip or conda.
Note
If you are using the docker environment then the additional libraries required to run the pre & post processing files are already installed as well as the notebooks examples and you can skip this step.
Pre/post processing libraries¶
If you are using conda, you will first put your self inside this environment run:
source activate gospl-package
On Windows the command is:
activate gospl-package
We will now install some additional libraries. For conda:
conda install pyvista pyevtk panel netCDF4 gdown
or via pip:
pip install pyvista pyevtk panel netCDF4 gdown
Meshing libraries¶
stripy¶
stripy is a Python interface to TRIPACK and STRIPACK Fortran code for (constrained) triangulation in Cartesian coordinates and on a sphere.
The library can be installed as a pip package:
pip install stripy
JIGSAW¶
JIGSAW Python is an unstructured mesh generator and tessellation library; designed to generate high-quality triangulations and polyhedral decompositions of general planar, surface and volumetric domains.
The library can be installed as a conda package:
conda install jigsaw
Installing it from source is also relatively straightforward once you have a C++ compiler and the cmake utility installed:
# Clone/download + unpack this repository.
git clone https://github.com/dengwirda/jigsaw-python.git
# Install the library...
cd jigsaw-python
python3 setup.py build_external
python3 setup.py install
Notebooks examples¶
The notebooks are available from the Github repository but you can also directly download them as a tar file from here or using the following command in your terminal:
gdown https://drive.google.com/uc?id=1SvRj27NBF4aA2E8svyniysQtDHuiVNIf
tar xvf notebooks.tar
Running the paleo-constrained example¶
The above example is a simpler version (smaller temporal extent and coarse resolution) of the simulation presented here.
Running the stratigraphic example¶
Step 3 - Advanced workflows¶
This section does not provide any dataset but some of Jupyter notebooks, post-processing functions and scripts that one can use to run and analyse complex paleo-forcing global scale models.
A set of scripts and proposed workflows to run a model with plate motion and surface remeshing conditions.