pastax 0.0.3

**P**arameterizable **A**uto-differentiable **S**imulators of ocean **T**rajectories in j**AX**.

pastax

Parameterizable Auto-differentiable Simulators of ocean Trajectories in jAX.

Installation

pastax is Pip-installable:

pip install pastax

Usage

Documentation is under construction but you can already have a look at the getting started notebook and the (messy) API documentation.

Work in progress

This package in under active developement and should still be considered as work in progress.

In particular, the following changes are considered:

• pastax.gridded
  • add support for C-grids,
  • maybe some refactoring of the structures,
• pastax.trajectory
  • use unxt.Quantity in place of Unitful (see unxt),
  • remove __add__ and like methods in favour of registered functions (see quax),
• pastax.simulator
  • improve how the product operation is performed between the vector field and the control (support for Location, Time or State objects) (see diffrax doc here),
  • add support/examples for interrupting the solve when a trajectory reaches land (see diffrax doc here).

And I should stress that the package lacks (unit-)tests for now.

Related projects

Several other open-source projects already exist with similar objectives. The closest ones are probably (Ocean)Parcels, OpenDrift and Drifters.jl.

Here is a (probably) non-comprehensive (and hopefuly correct, please reach-out if not) use-cases comparison between them:

• you use Python: go with pastax, OpenDrift or Parcels,
• you use Julia: go with Drifters.jl,
• you want I/O inter operability with xarray Datasets: go with pastax, OpenDrift, Parcels or Drifters.jl,
• you need support for Arakawa C-grid: go with OpenDrift, Parcels or Drifters.jl (but keep an eye on pastax as it might come in the future),
• you want some post-processing routines: go with Drifters.jl (but keep an eye on pastax as some might come in the future),
• you want a better control of the right-hand-side term of your Differential Equation: go with pastax (probably the most flexible) or Parcels,
• you solve Stochastic Differential Equations: go with pastax, OpenDrift or Parcels,
• you need a wide range of solvers: go with pastax or Drifters.jl (if you solve ODE),
• you want to calibrate your simulator on-line (i.e. by differenting through your simulator): go with pastax,
• you want to run on both CPUs and GPUs (or TPUs): go with pastax.

Worth mentionning that I did not compare runtime performances (especially for typical use-cases with OpenDrift, Parcels or Drifters.jl of advecting a very large amount of particules with the same velocity field).

I could also cite py-eddy-tracker, altough it targets more specifically eddy related routines.

Contributing

Contributions are welcomed! See CONTRIBUTING.md and CONDUCT.md to get started.