Simulator for the Lorenz 96 model. Uses Julia if available, otherwise falls back to numba, and finally to pure python.
pip install .
With this basic installation, you can use the gradient functions in L96_base.py.
For Julia support install the Julia language and diffeqpy.
If you don't have Julia, numba will be used instead. If numba also isn't present, interpreted numpy will be used.
If Julia and pydiffeq are installed, there will be about 1 minute Julia compile time on startup when using a python installation that has statically linked libraries. Unfortunately, this includes Anaconda. Fortunately, there is no compilation required when starting a new simulation, instantiating a new simulation object or changing simulation parameters.
solvers.ipynb compares 3 different numerical solvers and plots the results. These are similar, but not identical as L96 is a chaotic system.
inference.ipynb trains a neural network to perform Bayesian parameter inference with the APT algorithm. To use it, you will have to install delf.