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Getting Started

Installation

The main module DataAssimilationBenchmarks.jl declares global types and type constructors. These conventions are utilized in sub-modules that implement the core numerical solvers for ordinary and stochastic differential equations, solvers for data assimilation routines, and the core process model code for running twin experiments with benchmark models, collected in the methods and models sub-directories. Experiments define routines for driving standard benchmark case studies with NamedTuples as arguments to these methods defining the associated experimental hyper-parameters.

This parent module only serves to support the overhead of type declarations used thoughout the package and the functionality of the methods standalone is extremely limited. In order to get the full functionality of this package you will need to install the dev version. This provides access to the source code needed to create new experiments and to define performance benchmarks for these experiments.

Install a dev package

There are two ways to install a dev package of the repository. In either case, the installed version will be included in your

~/.julia/dev/

on Linux and the analogous directory with respect Windows and Mac systems.

Install the tagged stable version

To install the last tagged official release, you can use the following commands in the REPL

pkg> dev DataAssimilationBenchmarks

This version in the Julia General Registries will be the latest official release. However, this official release tends to lag behind the current version.

Install the up-to-date version

You can install the latest version from the main Github branch directly as follows:

pkg> dev https://github.com/cgrudz/DataAssimilationBenchmarks.jl

The master branch synchronizes with the up-to-date documentation and commits to the master branch are considered tested but not necessarily stable. As this package functions as a research framework, the master branch is in continuous development. If your use case is performing research of DA methods with this package, it is recommended to install and keep up-to-date with the current version of the master branch.

Repository structure

The repository is structured as follows:

  • src - contains the main parent module
    • models - contains code for defining the state and observation model equation for twin experiments
    • methods - contains DA solvers and general numerical routines for running twin experiments
    • experiments - contains the outer-loop scripts that set up twin experiments, and constructors for generating parameter grids
    • data - this is an input / output directory for the inputs to and ouptuts from experiments
    • analysis - contains auxilliary scripts for batch processing experiment results and for plotting (currently in Python, not fully integrated).
  • test - contains test cases for the package.
  • docs - contains the documenter files.